Fig 1: RACGAP1 silence inhibits tumor growth in vivo. A, Schematics of in vivo assay. B, Bioluminescence imaging was used to monitor the photon flux in the mouse brain. C, Quantitative analysis of photon flux in mice. D, Body weight analysis of mice. E, Overall survival of immunodeficient mice of Fig. B. F, H&E staining of mouse brain sections. G, IHC staining was used to detect the expression of RACGAP1, KI67 and PCNA. H, Correlation analysis of RACGAP1 with KI67 and PCNA, n = 11. Data are represented as mean ± SEM. ** P < 0.01;*** P < 0.001.
Fig 2: RACGAP1 is closely related to cell proliferation and cell cycle. A, Representative western blot images of RACGAP1 levels in T98G cells using RACGAP1 and GAPDH (loading control) antibodies. B, Representative immunofluorescence images after RACGAP1 interference; RACGAP1 (red), GFP (green, to monitor infection efficiency). Nuclei were stained with DAPI, Scale bar, 100 µm. C, Clustering of transcriptome samples based on mRNA sequences and volcano plots show DEGs between sh-NC and sh-RACGAP1 cells. D, GO enrichment was performed for significantly differentially expressed genes in C. E, GSEA enrichment was performed for all genes in C. F, Correlation plot shows the average correlation between RACGAP1 and the different functional states of glioma and glioblastoma. The size of the circle indicates the correlation coefficient. Negative correlations are depicted in blue, and positive correlations are depicted in red. G, CGGA data showed that gene expression of RACGAP1 was correlated with MKI67 all gliomas (R = 0.826, P = 1.26e-57). H, IHC staining and correlation analysis between RACGAP1 and Ki67. Scale bar, 25 µm.
Fig 3: RACGAP1 inhibits cell proliferation via MCM3. A, Heatmap shows the genes expression results of normal and RACGAP1 knockdown. B, Knockdown of RACGAP1 decreased the protein expression of MCM3 in T98G and LN229 cells. C, Representative IHC images of MCM3 in normal and sh-RACGAP1 tumoral tissues. Scale bar, 50 µm. D, TCGA and GTEx data revealed that MCM3 was highly expressed in GBM, GBMLGG, and LGG. E, IHC staining and statistical analysis of MCM in Normal and Glioma patients tissues. Scale bar, 25 μm. F, Survival probability of MCM3 in all primary glioma. G, CGGA database showed the relationship between gene expression of MCM3 and RACGAP1. H-I, Cell proliferation of sh-NC and sh-MCM3 was determined by monoclonal and EdU assay. J, WB showed the protein expression level in T98G and LN229 cells transfected with sh-NC and sh-MCM3. Data are represented as mean ± SEM. ** P < 0.01;*** P < 0.001; **** P < 0.0001.
Fig 4: T7 modified exosomes with siRACGAP accurately target glioma tissues. A, Schematic diagram of the construction of T7 modified exosomes carrying siRACGAP1 and the treatment protocol for glioma. B, Ex vivo photon flux monitoring of exosome distribution. Quantification of the relative expression levels is shown. Data are representative of three independent experiments. C, Representative fluorescent image analysis shows the distribution of exosomes in the orthotopic tumors of brain. Scale bar, 100 µm. Quantitative relative proportions of the positive areas are shown. D, Bioluminescence imaging was used to monitor the photon flux in the mouse brains on day 25. The xenograft tumor model was generated used glioma patient-derived cells. n = 8 for Ctrl, n = 8 for exo-nc, n = 8 for exo-siRACGAP1, and n = 8 for T7-siRACGAP1. E, Plot depicting photon flux in the mouse brains on day 25 in Fig.D. F, Overall survival of immunodeficient mice in Fig.D. Data were evaluated by Mantel-Cox test. G, The expression level of RACGAP1 was detected by IHC. Scale bar, 10 μm. H, H&E staining analysis of heart, liver, spleen, lung and kidney sections after injection of Saline, Exo-NC and T7-siRACGAP1 at day 25. Scale bar, 10 μm. Data are represented as mean ± SEM. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.
Fig 5: YY1 directly regulates RACGAP1. A, Survival probability of YY1 in all primary glioma. Data are presented as a Kaplan–Meier curve n, n (YY1hi) = 111, n (YY1lo) = 111, P = 0.003. B, Correlation analysis of gene expression. CGGA database was used to analyze the correlation between YY1 and RACGAP1 mRNA expression in glioma. The correlation coefficient is expressed as R, P = 4.5e–11. C, Binding site prediction of the transcription factor and promoter. The 2000-bp DNA sequence upstream of RACGAP1 was queried in NCBI. The transcription factor YY1 was selected by JASPAR, and the DNA sequence was input for prediction analysis in human tissues. Predicted sites with a correlation threshold >95% are annotated in blue. D, Dual luciferase reporter gene analysis of YY1 and RACGAP1 transcriptional regulation is shown. E, RACGAP1 expression was detected in T98G and LN229 cells derived from shNC and sh-YY1 by qRT-PCR analysis. Data are representative of three independent experiments. F, Western blot analysis of shNC and sh-YY1-derived T98G and LN229 cells using YY1, RacGAP1, and GAPDH (loading control) antibodies. Images are representative of three independent experiments. G-I, Representative immunofluorescence plots showed the relationship between RACGAP1 and YY1 expression in T98G and LN229 cells. YY1 was stained red, RACGAP1 was stained green, and nuclei were stained blue (DAPI). Scale bar, 10 µm. J, Western blot was used to detect protein expression of YY1, RACGAP1 and MCM3. Moreover, rescuing experiments confirmed that impeding the expression of YY1 could both down-regulate RACGAP1 and MCM3 expression. Besides, the decreased expression of MCM3 could be partially rescued by overexpression of RACGAP1. GAPDH was used as a loading control. Data are represented as mean ± SEM. * P < 0.05; ** P < 0.01; *** P < 0.001.
Supplier Page from Abcam for Anti-RACGAP1/MGCRACGAP antibody