Review

antibodies against dpp4 (R&D Systems)

R&D Systems is a verified supplier

R&D Systems manufactures this product

About

News

Press Release

Team

Advisors

Partners

Contact

Bioz Stars

Bioz vStars

Buy from Supplier

Structured Review

R&D Systems antibodies against dpp4
Antibodies Against Dpp4, supplied by R&D Systems, used in various techniques. Bioz Stars score: 93/100, based on 67 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/antibodies+against+dpp4/Mouse+DPPIV%2FCD26+Antibody/pm41187974-66-8-14
Average 93 stars, based on 67 article reviews

antibodies against dpp4 - by Bioz Stars, 2026-09

93/100 stars

Images

Incubation:

Article Title: Unique Role of Proximal Tubule Dipeptidyl Peptidase 4 on Blood Pressure, Renal Sodium Handling, and Na + /H + Exchanger Isoform 3 Phosphorylation.
Article Snippet: After two deionized water washes and demarcation with a hydrophobic barrier pen (PAP pen, catalog #AB2601, Abcam), nonspecific binding was blocked with 5% BSA and 0.25% Triton X- 100 in PBS for 30 min. .. Sections were incubated overnight at 4°C with primary antibodies against DPP4 (1:100, catalog #AF954, R&D Systems) and SGLT2 (1:100, catalog #20802, Bicell) in blocking solution. .. Following three 5- min PBS washes, sections were incubated for 1 h at room temperature with Alexa Fluor 488- conjugated donkey anti- goat IgG (catalog #A11055, Life Technologies/Thermo Fisher Scientific), Alexa Fluor 647- conjugated donkey antirabbit IgG (catalog #A31573, Life Technologies/Thermo Fisher Scientific), and DAPI (catalog #62248, Thermo Fisher Scientific) diluted 1:500 in blocking solution.

Article Title: Unique Role of Proximal Tubule Dipeptidyl Peptidase 4 on Blood Pressure, Renal Sodium Handling, and Na + /H + Exchanger Isoform 3 Phosphorylation
Article Snippet: After two deionized water washes and demarcation with a hydrophobic barrier pen (PAP pen, catalog #AB2601, Abcam), nonspecific binding was blocked with 5% BSA and 0.25% Triton X‐100 in PBS for 30 min. .. Sections were incubated overnight at 4°C with primary antibodies against DPP4 (1:100, catalog #AF954, R&D Systems) and SGLT2 (1:100, catalog #20802, Bicell) in blocking solution. .. Following three 5‐min PBS washes, sections were incubated for 1 h at room temperature with Alexa Fluor 488‐conjugated donkey anti‐goat IgG (catalog #A11055, Life Technologies/Thermo Fisher Scientific), Alexa Fluor 647‐conjugated donkey anti‐rabbit IgG (catalog # A31573 , Life Technologies/Thermo Fisher Scientific), and DAPI (catalog #62248, Thermo Fisher Scientific) diluted 1:500 in blocking solution.

Blocking Assay:

Image Search Results

The flow chart, differential expression analysis, ceRNA network construction, and PPI network construction. (A) The flow chart of our analysis. (B) Expression of DPP4 in 33 tumor types between normal tissues and tumor tissues. DPP4 expression was down-regulated in BRCA ( p < 0.001), CESC ( p < 0.05), CHOL ( p < 0.001), COAD ( p < 0.05), KICH ( p < 0.001), LUSC ( p < 0.001), PCPG ( p < 0.05), READ ( p < 0.01) and UCEC ( p < 0.01), while it was up-regulated in GBM ( p < 0.01), KIRC ( p < 0.001), KIRP ( p < 0.001), LIHC ( p < 0.001), LUAD ( p < 0.001), STAD ( p < 0.05) and THCA ( p < 0.001). (C) The ceRNA network of DPP4. There were three key miRNAs correlated with DPP4 and there were eight lncRNAs correlated with the key miRNAs. (D) The PPI network of DPP4. At the proteomic level, DPP4 was closely associated with FN1, CXCR4, CAV1, ITGB1, PTPRC, ADA, GCG, GIP, ACE2, and PRCP. ceRNA, competing endogenous RNA; lncRNA, long non-coding RNA; PPI, Protein-Protein Interaction. * p < 0.05, ** p < 0.01, *** p < 0.001.

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: The flow chart, differential expression analysis, ceRNA network construction, and PPI network construction. (A) The flow chart of our analysis. (B) Expression of DPP4 in 33 tumor types between normal tissues and tumor tissues. DPP4 expression was down-regulated in BRCA ( p < 0.001), CESC ( p < 0.05), CHOL ( p < 0.001), COAD ( p < 0.05), KICH ( p < 0.001), LUSC ( p < 0.001), PCPG ( p < 0.05), READ ( p < 0.01) and UCEC ( p < 0.01), while it was up-regulated in GBM ( p < 0.01), KIRC ( p < 0.001), KIRP ( p < 0.001), LIHC ( p < 0.001), LUAD ( p < 0.001), STAD ( p < 0.05) and THCA ( p < 0.001). (C) The ceRNA network of DPP4. There were three key miRNAs correlated with DPP4 and there were eight lncRNAs correlated with the key miRNAs. (D) The PPI network of DPP4. At the proteomic level, DPP4 was closely associated with FN1, CXCR4, CAV1, ITGB1, PTPRC, ADA, GCG, GIP, ACE2, and PRCP. ceRNA, competing endogenous RNA; lncRNA, long non-coding RNA; PPI, Protein-Protein Interaction. * p < 0.05, ** p < 0.01, *** p < 0.001.

Article Snippet: Primary antibodies against human DPP4 (1:200; Affinity Biosciences Cat# DF12387, RRID: AB_2845192) were then introduced, followed by secondary antibodies conjugated with horseradish peroxidase (HRP).

Techniques: Quantitative Proteomics, Expressing

Survival analysis and univariate Cox regression analysis in pan-cancer. (A) Survival analysis of DPP4 in pan-cancer. K-M survival curves indicated DPP4 was positively correlated with OS in KIRC ( p < 0.001), with DSS in KIRC ( p < 0.001), and with DFS in PRAD ( p < 0.001). (B) Univariate Cox model of OS. DPP4 expression was a risk factor in DLBC (HR = 2.757, 95%CI = 1.066-7.127, p = 0.036), LAML (HR = 2.757, 95%CI = 1.066-7.127, p = 0.036), LGG (HR = 3.474, 95%CI = 2.617-4.611, p < 0.001), and LUSC (HR = 1.118, 95%CI = 1.000-2.014, p < 0.049), and it was a protective factor in KIRC (HR = 0.787, 95%CI = 0.716-0.865, p < 0.001), KIRP (HR = 0.789, 95%CI = 0.675-0.921, p = 0.003), LUAD (HR = 0.911, 95%CI = 0.837-0.992, p = 0.031), THCA (HR = 0.701, 95%CI = 0.553-0.887, p = 0.003), and THYM (HR = 0.440, 95%CI = 0.233-0.830, p = 0.011). (C) Univariate Cox model of DFS. DPP4 expression was a risk factor in LGG (HR = 4.698, 95%CI = 1.068-20.654, p = 0.041) and UCS (HR = 1.542, 95%CI = 1.004-2.370, p = 0.048), while it was a protective factor in PRAD (HR = 0.727, 95%CI = 0.567-0.931, p = 0.012). (D) Univariate Cox model in DSS. DPP4 expression was a risk factor in LGG (HR = 3.573, 95%CI = 2.672-4.778, p < 0.001), BRCA (HR = 1.383, 95%CI = 1.133-1.689, p = 0.001), DLBC (HR = 7.111, 95%CI = 1.469-34.432, p = 0.015), and ACC (HR = 1.343, 95%CI = 1.036-1.742, p = 0.026), while it was a protective factor in KIRC (HR = 0.703, 95%CI = 0.630-0.784, p < 0.001), KIRP (HR = 0.706, 95%CI = 0.595-0.838, p < 0.001), THCA (HR = 0.593, 95%CI = 0.419-0.839, p = 0.003), and LUAD (HR = 0.872, 95%CI = 0.782-0.971, p = 0.013). (E) Univariate Cox model in PFS. DPP4 expression was a risk factor in LGG (HR = 2.442, 95%CI = 1.884-3.163, p < 0.001), PCPG (HR = 1.832, 95%CI = 1.041-3.226, p = 0.036), and LUSC (HR = 1.137, 95%CI = 1.005-1.285, p = 0.041), while it was a protective factor in KIRC (HR = 0.787, 95%CI = 0.717-0.864, p < 0.001), PRAD (HR = 0.736, 95%CI = 0.638-0.850, p < 0.001), KIRP (HR = 0.818, 95%CI = 0.711-0.942, p = 0.005), PAAD (HR = 0.794, 95%CI = 0.659-0.956, p = 0.015), and MESO (HR = 0.836, 95%CI = 0.716-0.976, p = 0.023). OS, Overall Survival; DFS, Disease-Free Survival; DSS, Disease-Specific Survival; PFS, Progression-Free Survival. *p < 0.05, **p < 0.01, ***p < 0.001 .

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: Survival analysis and univariate Cox regression analysis in pan-cancer. (A) Survival analysis of DPP4 in pan-cancer. K-M survival curves indicated DPP4 was positively correlated with OS in KIRC ( p < 0.001), with DSS in KIRC ( p < 0.001), and with DFS in PRAD ( p < 0.001). (B) Univariate Cox model of OS. DPP4 expression was a risk factor in DLBC (HR = 2.757, 95%CI = 1.066-7.127, p = 0.036), LAML (HR = 2.757, 95%CI = 1.066-7.127, p = 0.036), LGG (HR = 3.474, 95%CI = 2.617-4.611, p < 0.001), and LUSC (HR = 1.118, 95%CI = 1.000-2.014, p < 0.049), and it was a protective factor in KIRC (HR = 0.787, 95%CI = 0.716-0.865, p < 0.001), KIRP (HR = 0.789, 95%CI = 0.675-0.921, p = 0.003), LUAD (HR = 0.911, 95%CI = 0.837-0.992, p = 0.031), THCA (HR = 0.701, 95%CI = 0.553-0.887, p = 0.003), and THYM (HR = 0.440, 95%CI = 0.233-0.830, p = 0.011). (C) Univariate Cox model of DFS. DPP4 expression was a risk factor in LGG (HR = 4.698, 95%CI = 1.068-20.654, p = 0.041) and UCS (HR = 1.542, 95%CI = 1.004-2.370, p = 0.048), while it was a protective factor in PRAD (HR = 0.727, 95%CI = 0.567-0.931, p = 0.012). (D) Univariate Cox model in DSS. DPP4 expression was a risk factor in LGG (HR = 3.573, 95%CI = 2.672-4.778, p < 0.001), BRCA (HR = 1.383, 95%CI = 1.133-1.689, p = 0.001), DLBC (HR = 7.111, 95%CI = 1.469-34.432, p = 0.015), and ACC (HR = 1.343, 95%CI = 1.036-1.742, p = 0.026), while it was a protective factor in KIRC (HR = 0.703, 95%CI = 0.630-0.784, p < 0.001), KIRP (HR = 0.706, 95%CI = 0.595-0.838, p < 0.001), THCA (HR = 0.593, 95%CI = 0.419-0.839, p = 0.003), and LUAD (HR = 0.872, 95%CI = 0.782-0.971, p = 0.013). (E) Univariate Cox model in PFS. DPP4 expression was a risk factor in LGG (HR = 2.442, 95%CI = 1.884-3.163, p < 0.001), PCPG (HR = 1.832, 95%CI = 1.041-3.226, p = 0.036), and LUSC (HR = 1.137, 95%CI = 1.005-1.285, p = 0.041), while it was a protective factor in KIRC (HR = 0.787, 95%CI = 0.717-0.864, p < 0.001), PRAD (HR = 0.736, 95%CI = 0.638-0.850, p < 0.001), KIRP (HR = 0.818, 95%CI = 0.711-0.942, p = 0.005), PAAD (HR = 0.794, 95%CI = 0.659-0.956, p = 0.015), and MESO (HR = 0.836, 95%CI = 0.716-0.976, p = 0.023). OS, Overall Survival; DFS, Disease-Free Survival; DSS, Disease-Specific Survival; PFS, Progression-Free Survival. *p < 0.05, **p < 0.01, ***p < 0.001 .

Techniques: Expressing

Relationship of DPP4 expression with TME in pan-cancer. (A) Correlation of DPP4 expression with MSI. It was positively correlated with MSI in COAD ( p < 0.001), ESCA ( p < 0.05), and KIRC ( p < 0.01), while it was negatively related in DLBC ( p < 0.001), HNSC ( p < 0.05), LUSC ( p < 0.001), PRAD ( p < 0.01), SKCM ( p < 0.001), and UCS ( p < 0.05). (B) Correlation of DPP4 expression with TMB. It was positively correlated with TMB in LAML ( p < 0.01), SARC ( p < 0.05), ESCA ( p < 0.001), KIRP ( p < 0.001), COAD ( p < 0.01), UCEC ( p < 0.05), GBM ( p < 0.05), LIHC ( p < 0.05), and OV ( p < 0.05), while it was negatively correlated in THYM ( p < 0.001), LUSC ( p < 0.001), CESC ( p < 0.05), PRAD ( p < 0.001), BRCA ( p < 0.001), and LUAD ( p < 0.05). (C) The relationship between DPP4 expression and immune-related genes. There is a significant association between DPP4 expression and immune-related genes across various cancers, particularly with NRP1 and HHLA2. Additionally, the majority of immune genes showed a positive correlation with DPP4 expression in BLCA, BRCA, LGG, SKCM, and THCA. (D) In PRAD, DPP4 expression was positively correlated with T cells CD4 memory resting (R = 0.18, p < 0.001), while it was negatively correlated with T cells CD8 (R = -0.19, p < 0.001), and T cells regulatory (R = -0.19, p < 0.001). MSI, Microsatellite Instability; TMB, Tumor Mutation Burden; TME, Tumor Microenvironment. *p < 0.05, **p < 0.01, ***p < 0.001 .

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: Relationship of DPP4 expression with TME in pan-cancer. (A) Correlation of DPP4 expression with MSI. It was positively correlated with MSI in COAD ( p < 0.001), ESCA ( p < 0.05), and KIRC ( p < 0.01), while it was negatively related in DLBC ( p < 0.001), HNSC ( p < 0.05), LUSC ( p < 0.001), PRAD ( p < 0.01), SKCM ( p < 0.001), and UCS ( p < 0.05). (B) Correlation of DPP4 expression with TMB. It was positively correlated with TMB in LAML ( p < 0.01), SARC ( p < 0.05), ESCA ( p < 0.001), KIRP ( p < 0.001), COAD ( p < 0.01), UCEC ( p < 0.05), GBM ( p < 0.05), LIHC ( p < 0.05), and OV ( p < 0.05), while it was negatively correlated in THYM ( p < 0.001), LUSC ( p < 0.001), CESC ( p < 0.05), PRAD ( p < 0.001), BRCA ( p < 0.001), and LUAD ( p < 0.05). (C) The relationship between DPP4 expression and immune-related genes. There is a significant association between DPP4 expression and immune-related genes across various cancers, particularly with NRP1 and HHLA2. Additionally, the majority of immune genes showed a positive correlation with DPP4 expression in BLCA, BRCA, LGG, SKCM, and THCA. (D) In PRAD, DPP4 expression was positively correlated with T cells CD4 memory resting (R = 0.18, p < 0.001), while it was negatively correlated with T cells CD8 (R = -0.19, p < 0.001), and T cells regulatory (R = -0.19, p < 0.001). MSI, Microsatellite Instability; TMB, Tumor Mutation Burden; TME, Tumor Microenvironment. *p < 0.05, **p < 0.01, ***p < 0.001 .

Techniques: Expressing, Mutagenesis

Drug sensitivity prediction for DPP4 in (A) CellMiner, (B) CTRP and (C) GDSC databases. The expression of DPP4 was negatively related with drug sensitivity of most drugs. However, several drugs were positively related with DPP4 expression, including perifosine and adavosertib from CellMiner, dasatinib and saracatinib from CTRP, and cetuximab and crizotinib from GDSC. (D) The molecular docking analysis of DPP4 and dasatinib. (E) The molecular docking analysis of DPP4 and midostaurin. (F) The molecular docking analysis of DPP4 and saracatinib. (G) The molecular docking analysis of DPP4 and selumetinib. The possible binding sites were illustrated. (H) RMSD values of the protein-ligand complexes over time. The DPP4-Saracatinib complex reached equilibrium after 20 ns, with its RMSD fluctuating around 2.2 Å. The DPP4-Selumetinib complex reached equilibrium after 20 ns, fluctuating around 4.1 Å. The DPP4-Dasatinib complex reached equilibrium after 20 ns, fluctuating around 2.0 Å. The DPP4-Midostaurin complex reached equilibrium after 30 ns, fluctuating around 2.2 Å. (I) Rg of the protein-ligand complexes over time. All complex systems exhibited only minor fluctuations throughout the simulation. (J) SASA of the protein-ligand complexes over time. The results indicate that the SASA of the complexes did not change significantly after ligand binding to DPP4. (K) RMSF of the protein-ligand complexes. The RMSF values for all complexes were relatively low, with most residues fluctuating below 3 Å. RMSD, Root Mean Square Deviation; RMSF, Root-Mean-Square Fluctuation; Rg, Radius of gyration; SASA, Solvent-Accessible Surface Area.

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: Drug sensitivity prediction for DPP4 in (A) CellMiner, (B) CTRP and (C) GDSC databases. The expression of DPP4 was negatively related with drug sensitivity of most drugs. However, several drugs were positively related with DPP4 expression, including perifosine and adavosertib from CellMiner, dasatinib and saracatinib from CTRP, and cetuximab and crizotinib from GDSC. (D) The molecular docking analysis of DPP4 and dasatinib. (E) The molecular docking analysis of DPP4 and midostaurin. (F) The molecular docking analysis of DPP4 and saracatinib. (G) The molecular docking analysis of DPP4 and selumetinib. The possible binding sites were illustrated. (H) RMSD values of the protein-ligand complexes over time. The DPP4-Saracatinib complex reached equilibrium after 20 ns, with its RMSD fluctuating around 2.2 Å. The DPP4-Selumetinib complex reached equilibrium after 20 ns, fluctuating around 4.1 Å. The DPP4-Dasatinib complex reached equilibrium after 20 ns, fluctuating around 2.0 Å. The DPP4-Midostaurin complex reached equilibrium after 30 ns, fluctuating around 2.2 Å. (I) Rg of the protein-ligand complexes over time. All complex systems exhibited only minor fluctuations throughout the simulation. (J) SASA of the protein-ligand complexes over time. The results indicate that the SASA of the complexes did not change significantly after ligand binding to DPP4. (K) RMSF of the protein-ligand complexes. The RMSF values for all complexes were relatively low, with most residues fluctuating below 3 Å. RMSD, Root Mean Square Deviation; RMSF, Root-Mean-Square Fluctuation; Rg, Radius of gyration; SASA, Solvent-Accessible Surface Area.

Techniques: Expressing, Binding Assay, Ligand Binding Assay, Solvent

A single-cell transcriptomic atlas of DPP4 in prostate cancer. (A) UMAP dimension reduction plot exhibiting 386,664 single-cell transcriptomes across nine major cell lineages (B cell, DC, endothelial cell, epithelial cell, fibroblast, mast cell, mono_macro, NK cell, and T cell) and 18 minor subtypes (luminal, basal, NE, NK cell, CD4+ T cell, CD8+ T cell, B cell, plasma cell, monocyte, macrophage, cDC1, cDC2, pDC, mast cell, fibroblast, SMC, pericyte, and endothelial cell). (B) Bubble plots depicting the feature expression of different marker genes in nine major cell subtypes. (C) UMAP dimension reduction plots by Grade1-5. (D) Bar plot demonstrating that the proportion of epithelial cells varied greatly in different grades of prostate cancer. (E) Stacked bar plots highlighting the enrichment of upregulated DEGs in epithelial cells within non-metastatic prostate cancer. (F, G) DPP4 expression was exclusively expressed in luminal cells. (H) Violin plots comparing DPP4 expression levels across ISUP grades, showing significantly higher expression in low-grade groups (p < 0.001). (I) Violin plots showing DPP4 expression across clinical T stages, indicating a significant downregulation in advanced stages (p < 0.001). (J) The interaction network illustrating the cellular communications of DPP4+ epithelial cells. (K) Heatmap summarizing the total interaction numbers, highlighting that DPP4+ epithelial cells exhibit significant communication with fibroblasts. (L) Volcano plot showing genes significantly perturbed by virtual DPP4 knockout in epithelial cells. (M) Functional enrichment analysis of the significantly perturbed genes following virtual KO of DPP4. UMAP, Uniform Manifold Approximation and Projection; AJCC, American Joint Committee on Cancer; DEG, Differential Expressed Genes; KO, Knockout.

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: A single-cell transcriptomic atlas of DPP4 in prostate cancer. (A) UMAP dimension reduction plot exhibiting 386,664 single-cell transcriptomes across nine major cell lineages (B cell, DC, endothelial cell, epithelial cell, fibroblast, mast cell, mono_macro, NK cell, and T cell) and 18 minor subtypes (luminal, basal, NE, NK cell, CD4+ T cell, CD8+ T cell, B cell, plasma cell, monocyte, macrophage, cDC1, cDC2, pDC, mast cell, fibroblast, SMC, pericyte, and endothelial cell). (B) Bubble plots depicting the feature expression of different marker genes in nine major cell subtypes. (C) UMAP dimension reduction plots by Grade1-5. (D) Bar plot demonstrating that the proportion of epithelial cells varied greatly in different grades of prostate cancer. (E) Stacked bar plots highlighting the enrichment of upregulated DEGs in epithelial cells within non-metastatic prostate cancer. (F, G) DPP4 expression was exclusively expressed in luminal cells. (H) Violin plots comparing DPP4 expression levels across ISUP grades, showing significantly higher expression in low-grade groups (p < 0.001). (I) Violin plots showing DPP4 expression across clinical T stages, indicating a significant downregulation in advanced stages (p < 0.001). (J) The interaction network illustrating the cellular communications of DPP4+ epithelial cells. (K) Heatmap summarizing the total interaction numbers, highlighting that DPP4+ epithelial cells exhibit significant communication with fibroblasts. (L) Volcano plot showing genes significantly perturbed by virtual DPP4 knockout in epithelial cells. (M) Functional enrichment analysis of the significantly perturbed genes following virtual KO of DPP4. UMAP, Uniform Manifold Approximation and Projection; AJCC, American Joint Committee on Cancer; DEG, Differential Expressed Genes; KO, Knockout.

Techniques: Single Cell, Clinical Proteomics, Expressing, Marker, Knock-Out, Functional Assay

Higher DPP4 expression was correlated with better prognosis in prostate cancer. (A) The inclusion and exclusion criteria of the cohort. (B) IHC scores revealed that normal tissue exhibited significantly higher DPP4 expression compared to tumor tissues ( p < 0.001). (C) Representative IHC images of both prostate cancer and normal tissues from the cohort demonstrated this difference visually. (D) The Chi square test showed that DPP4 expression was associated with WHO/ISUP grade ( p = 0.03). (E) The K-M survival curve indicated that higher DPP4 expression was not significantly correlated with OS and PFS ( p > 0.05). However, DPP4 expression tended to be a protective factor. (F) Multivariate Cox regression analysis indicated that high DPP4 expression was an independent protective factor for OS in prostate cancer patients (HR = 0.052, 95%CI = 0.0041 - 0.65, p = 0.02). IHC, Immunohistochemical; OS, Overall Survival; PFS, Progression-Free Survival.

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: Higher DPP4 expression was correlated with better prognosis in prostate cancer. (A) The inclusion and exclusion criteria of the cohort. (B) IHC scores revealed that normal tissue exhibited significantly higher DPP4 expression compared to tumor tissues ( p < 0.001). (C) Representative IHC images of both prostate cancer and normal tissues from the cohort demonstrated this difference visually. (D) The Chi square test showed that DPP4 expression was associated with WHO/ISUP grade ( p = 0.03). (E) The K-M survival curve indicated that higher DPP4 expression was not significantly correlated with OS and PFS ( p > 0.05). However, DPP4 expression tended to be a protective factor. (F) Multivariate Cox regression analysis indicated that high DPP4 expression was an independent protective factor for OS in prostate cancer patients (HR = 0.052, 95%CI = 0.0041 - 0.65, p = 0.02). IHC, Immunohistochemical; OS, Overall Survival; PFS, Progression-Free Survival.

Techniques: Expressing, Immunohistochemical staining

Dasatinib and midostaurin regulated DPP4 expression. (A) IC50 of 22Rv1 and C4–2 treated with dasatinib tested by CCK-8 assays. (B) IC50 of 22Rv1 and C4–2 treated with midostarin tested by CCK-8 assays. (C) Dasatinib treatment significantly increased DPP4 expression in C4–2 cells ( p = 0.0042, primer 1; p = 0.0029, primer 2). In contrast, midostaurin treatment reduced DPP4 expression in both cell lines (C4-2: p = 0.0218, primer 1; 22Rv1: p = 0.0172, primer 1; p = 0.0002, primer 2). IC50, Half maximal inhibitory concentration; CCK-8, Cell Counting Kit-8.

Journal: Frontiers in Immunology

Article Title: Integrative pan-cancer analysis of dipeptidyl peptidase 4 with clinical and in vitro validation in prostate cancer

doi: 10.3389/fimmu.2026.1616889

Figure Lengend Snippet: Dasatinib and midostaurin regulated DPP4 expression. (A) IC50 of 22Rv1 and C4–2 treated with dasatinib tested by CCK-8 assays. (B) IC50 of 22Rv1 and C4–2 treated with midostarin tested by CCK-8 assays. (C) Dasatinib treatment significantly increased DPP4 expression in C4–2 cells ( p = 0.0042, primer 1; p = 0.0029, primer 2). In contrast, midostaurin treatment reduced DPP4 expression in both cell lines (C4-2: p = 0.0218, primer 1; 22Rv1: p = 0.0172, primer 1; p = 0.0002, primer 2). IC50, Half maximal inhibitory concentration; CCK-8, Cell Counting Kit-8.

Techniques: Expressing, CCK-8 Assay, Concentration Assay, Cell Counting

Baseline characteristics of the control and Hashimoto thyroiditis groups

Journal: The Journal of Clinical Endocrinology and Metabolism

Article Title: Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

doi: 10.1210/clinem/dgad723

Figure Lengend Snippet: Baseline characteristics of the control and Hashimoto thyroiditis groups

Article Snippet: Thyroid sections were incubated with primary antibody against DPP4 (ab215711, Abcam, RRID: AB_2734752) and IHC Detection Reagent (PV-8000, ZSGB-BIO).

Techniques: Concentration Assay, Activity Assay

The serum concentration and activity of DPP4 in the control and HT groups. A, Serum DPP4 concentration. B, Serum DPP4 activity. Data are shown as mean ± SD. DPP4, dipeptidyl peptidase-4; HT, Hashimoto thyroiditis.

Journal: The Journal of Clinical Endocrinology and Metabolism

Article Title: Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

doi: 10.1210/clinem/dgad723

Figure Lengend Snippet: The serum concentration and activity of DPP4 in the control and HT groups. A, Serum DPP4 concentration. B, Serum DPP4 activity. Data are shown as mean ± SD. DPP4, dipeptidyl peptidase-4; HT, Hashimoto thyroiditis.

Article Snippet: Thyroid sections were incubated with primary antibody against DPP4 (ab215711, Abcam, RRID: AB_2734752) and IHC Detection Reagent (PV-8000, ZSGB-BIO).

Techniques: Concentration Assay, Activity Assay

Thyroid DPP4 expression and its association with inflammatory factors. A, mRNA expression of DPP4 and inflammatory factors in the thyroid of the control and HT groups. B, The protein expression of thyroid DPP4 in the thyroid of the control and HT groups. GAPDH was used as a loading control. C, The DPP4 activity in the thyroid of the control and HT groups. D, Correlation analysis of the DPP4 mRNA expression and the mRNA expression of inflammatory factors in thyroid tissue. E, Correlation analysis between the mRNA expression and activity of DPP4 in thyroid tissue and the serum concentration and activity of DPP4. F, Correlation analysis between the mRNA expression of DPP4 and serum TPOAb/TgAb levels. G, Enhanced DPP4 expression on lymphocytes in thyroid tissue of HT patients. Representative images of HE and IHC staining of DPP4 in the thyroid sections from control and HT patients (200× magnification, light microscope). Scale bar = 100μm. Data are shown as mean ± SEM. * P less than .05, ** P less than .01, *** P less than .001. DPP4, dipeptidyl peptidase-4; HE, hematoxylin and eosin staining; HT, Hashimoto thyroiditis; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IHC, immunohistochemistry; IL-1β, interleukin 1β; INF-γ, interferon γ; mRNA, messenger RNA; TNF-α, tumor necrosis factor α.

Journal: The Journal of Clinical Endocrinology and Metabolism

Article Title: Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

doi: 10.1210/clinem/dgad723

Figure Lengend Snippet: Thyroid DPP4 expression and its association with inflammatory factors. A, mRNA expression of DPP4 and inflammatory factors in the thyroid of the control and HT groups. B, The protein expression of thyroid DPP4 in the thyroid of the control and HT groups. GAPDH was used as a loading control. C, The DPP4 activity in the thyroid of the control and HT groups. D, Correlation analysis of the DPP4 mRNA expression and the mRNA expression of inflammatory factors in thyroid tissue. E, Correlation analysis between the mRNA expression and activity of DPP4 in thyroid tissue and the serum concentration and activity of DPP4. F, Correlation analysis between the mRNA expression of DPP4 and serum TPOAb/TgAb levels. G, Enhanced DPP4 expression on lymphocytes in thyroid tissue of HT patients. Representative images of HE and IHC staining of DPP4 in the thyroid sections from control and HT patients (200× magnification, light microscope). Scale bar = 100μm. Data are shown as mean ± SEM. * P less than .05, ** P less than .01, *** P less than .001. DPP4, dipeptidyl peptidase-4; HE, hematoxylin and eosin staining; HT, Hashimoto thyroiditis; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IHC, immunohistochemistry; IL-1β, interleukin 1β; INF-γ, interferon γ; mRNA, messenger RNA; TNF-α, tumor necrosis factor α.

Article Snippet: Thyroid sections were incubated with primary antibody against DPP4 (ab215711, Abcam, RRID: AB_2734752) and IHC Detection Reagent (PV-8000, ZSGB-BIO).

Techniques: Expressing, Activity Assay, Concentration Assay, Immunohistochemistry, Light Microscopy, Staining

Overview of single-cell transcriptomic analysis. A, UMAP plot of the 11 identified cell types. B, Dot plots showing the 28 marker gene expressions among the 11 cell types. The size of dots represents the proportion of cells expressing the particular marker gene, and the color spectrum indicates the mean expression levels of the marker gene (log transformed). C, Relative proportion of each cell type. D, The DPP4 expression in the CT and HT group. E, UMAP plot highlighting the gene expression of IL7R (T cell) and DPP4. F, DPP4 expression in different T-cell cluster between HT and control groups. G, The cell-cell communications heat map based on the relative expression of ligand-receptor pairs. CT, control group; DPP4, dipeptidyl peptidase-4; HT, Hashimoto thyroiditis; UMAP, uniform maximal approximation projection.

Journal: The Journal of Clinical Endocrinology and Metabolism

Article Title: Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

doi: 10.1210/clinem/dgad723

Figure Lengend Snippet: Overview of single-cell transcriptomic analysis. A, UMAP plot of the 11 identified cell types. B, Dot plots showing the 28 marker gene expressions among the 11 cell types. The size of dots represents the proportion of cells expressing the particular marker gene, and the color spectrum indicates the mean expression levels of the marker gene (log transformed). C, Relative proportion of each cell type. D, The DPP4 expression in the CT and HT group. E, UMAP plot highlighting the gene expression of IL7R (T cell) and DPP4. F, DPP4 expression in different T-cell cluster between HT and control groups. G, The cell-cell communications heat map based on the relative expression of ligand-receptor pairs. CT, control group; DPP4, dipeptidyl peptidase-4; HT, Hashimoto thyroiditis; UMAP, uniform maximal approximation projection.

Article Snippet: Thyroid sections were incubated with primary antibody against DPP4 (ab215711, Abcam, RRID: AB_2734752) and IHC Detection Reagent (PV-8000, ZSGB-BIO).

Techniques: Marker, Expressing, Transformation Assay

Review

antibodies against dpp4 (R&D Systems)

Structured Review

Images

Related Articles

Incubation:

Blocking Assay:

Similar Products

Image Search Results