Journal: Frontiers in Immunology

Article Title: Plasma PFDN2 suppresses head and neck squamous cell carcinoma progression by restricting CD64 on monocyte-driven inflammatory microenvironments

doi: 10.3389/fimmu.2026.1791776

Figure Lengend Snippet: Plasma protein PFDN2 may regulate hypopharyngeal carcinoma through the immune cell phenotype CD64 on monocyte. (A) Forest plot of the Mendelian randomization analysis and leave-one-out sensitivity analysis. (B) Leave-one-out sensitivity analysis of the Mendelian randomization between PFDN2 and hypopharyngeal carcinoma. (C) Leave-one-out sensitivity analysis of the Mendelian randomization between CD64 on monocyte and hypopharyngeal carcinoma. (D) Leave-one-out sensitivity analysis of the Mendelian randomization between PFDN2 and CD64 on monocyte.

Article Snippet: The basilar membrane was immunolabeled with rabbit anti-PFDN2 (#13053-1-AP, Proteintech, China) and anti-CD64 (FCGR1A) (#27563-1-AP, Proteintech, China) and incubated overnight at 4 °C.

Techniques: Clinical Proteomics

Journal: Frontiers in Immunology

Article Title: Plasma PFDN2 suppresses head and neck squamous cell carcinoma progression by restricting CD64 on monocyte-driven inflammatory microenvironments

doi: 10.3389/fimmu.2026.1791776

Figure Lengend Snippet: Single-cell RNA sequencing validation of the relationship between PFDN2 and CD64 on monocyte distribution in HNSC. (A) Overall workflow schematic. (B) t-SNE plot of scRNA-seq data from three laryngeal carcinoma samples and normal adjacent tissue samples, annotated with 18 cell clusters. (C, D) Distribution of PFDN2- and FCGR1A-expressing cell clusters. (E) t-SNE plot of scRNA-seq data from three laryngeal carcinoma samples and normal adjacent tissue samples, annotated with 8 cell clusters. (F–I) Distribution of PFDN2- and FCGR1A-expressing cell clusters. (J) Proportion of gene expression changes following PFDN2 knockout in HNSC explored using the scTenifoldKnk approach. (K, L) Distribution of differentially regulated genes.

Article Snippet: The basilar membrane was immunolabeled with rabbit anti-PFDN2 (#13053-1-AP, Proteintech, China) and anti-CD64 (FCGR1A) (#27563-1-AP, Proteintech, China) and incubated overnight at 4 °C.

Techniques: Single Cell, RNA Sequencing, Biomarker Discovery, Expressing, Gene Expression, Knock-Out

Journal: Frontiers in Immunology

Article Title: Plasma PFDN2 suppresses head and neck squamous cell carcinoma progression by restricting CD64 on monocyte-driven inflammatory microenvironments

doi: 10.3389/fimmu.2026.1791776

Figure Lengend Snippet: GROMACS-based molecular dynamics simulations of the PFDN2-CD64 docking complex and its mutant counterpart. (A–C) Structural representation of the initial protein-protein docking complex, in which PFDN2 is depicted in blue and CD64 in yellow; intermolecular hydrogen bonds are indicated by dashed lines. (D, F) Two- and three-dimensional free energy landscapes of the wild-type docking complex, and (E, G) the corresponding landscapes of the mutant complex. In these plots, the x-axis denotes the root-mean-square deviation (RMSD) and the y-axis represents the radius of gyration (Rg), both of which describe the conformational energy states of the complex. The color gradient reflects relative free energy, ranging from low (blue) to high (red). Lower-energy basins indicate greater binding stability, whereas the expanded high-energy regions observed in the mutant complex suggest reduced structural stability. (H) Comparison of hydrogen bond dynamics between the wild-type and mutant complexes, showing a marked reduction in intermolecular hydrogen bonds in the mutant complex (red), thereby confirming the successful generation of the mutant model. (I) RMSD of the original docking model, with CD64 in black, PFDN2 in red, and the overall docking model in blue, indicating that post-docking, the energy fluctuation range is small and remains in a low-energy state, suggesting stable binding. (J) RMSD comparison between the wild-type (black) and mutant (red) complexes, demonstrating larger fluctuations and persistent high-energy conformations in the mutant, indicative of decreased stability. (K) Radius of gyration (Rg) profiles of the wild-type (black) and mutant (red) complexes, further revealing enhanced structural fluctuations and higher energy states in the mutant model. (L) Solvent-accessible surface area (SASA) analysis of the mutant complex (red), which exhibits broader fluctuations, reflecting increased conformational instability. (M, N) Root-mean-square fluctuation (RMSF) analyses, showing that the mutant complex (red) displays greater residue-level flexibility, further supporting its reduced structural stability.

Article Snippet: The basilar membrane was immunolabeled with rabbit anti-PFDN2 (#13053-1-AP, Proteintech, China) and anti-CD64 (FCGR1A) (#27563-1-AP, Proteintech, China) and incubated overnight at 4 °C.

Techniques: Mutagenesis, Binding Assay, Comparison, Solvent, Residue

Journal: Frontiers in Immunology

Article Title: Plasma PFDN2 suppresses head and neck squamous cell carcinoma progression by restricting CD64 on monocyte-driven inflammatory microenvironments

doi: 10.3389/fimmu.2026.1791776

Figure Lengend Snippet: Immunofluorescence analysis of PFDN2 and CD64 expression in laryngeal carcinoma and control tissues. Tissue samples from 27 patients with laryngeal carcinoma and 8 normal control tissues were collected. Two tissue cores from each specimen were included in tissue microarray construction, yielding a total of 54 laryngeal carcinoma cores and 16 normal control cores. (A) Representative immunofluorescence images showing the expression of PFDN2 and CD64 in laryngeal carcinoma tissues and corresponding control tissues. (B) Quantitative analysis of PFDN2 immunofluorescence signals. (C) Quantitative analysis of CD64 immunofluorescence signals. (*, p<0.05).

Article Snippet: The basilar membrane was immunolabeled with rabbit anti-PFDN2 (#13053-1-AP, Proteintech, China) and anti-CD64 (FCGR1A) (#27563-1-AP, Proteintech, China) and incubated overnight at 4 °C.

Techniques: Immunofluorescence, Expressing, Control, Microarray