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human gbm cell line u87 gbm (ATCC)

Name: human gbm cell line u87 gbm
Brand: ATCC
Rating: 99 (10908 reviews)

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ATCC human gbm cell line u87 gbm
Human Gbm Cell Line U87 Gbm, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 10908 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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human gbm cell line u87 gbm - by Bioz Stars, 2026-03

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Overexpression of VMP1 promotes tumor growth. (A) Representative western blot image validating exogenous overexpression of VMP1 in U87 and U251 cell lines. (B) U87 subcutaneous xenografts of overexpressed VMP1 (VMP1‐OE) and control vector at Day 17 post‐injection ( n = 9). (C) The weight of the tumor grafts. (D) Quantification of normalized tumor weights of VMP1‐OE and vector (measured every 4 days from Day 3) ( n = 9). (E) Bioluminescence imaging of U87 VMP1‐OE and vector orthotopic xenografts at different time points from Day 3 to Day 17 ( n = 6). (F) Representative images of hematoxylin and eosin‐stained sections at Day 17 post‐injection. Tumor is indicated within the dashed line. Scale bar, 1000 µm. (G) Relative total photon flux of bioluminescence in mice with U87 VMP1‐OE and vector. (H) Kaplan–Meier survival analysis of mice with U87 VMP1‐OE and vector intracranial xenografts ( n = 6). (I) Top, immunohistochemical staining showing Ki67‐positive cells in subcutaneous and intracranial xenografts. Scale bar, 100 µm. Bottom, quantification of Ki67‐positive cells (%) in subcutaneous and intracranial models. * p < 0.05; ** p < 0.01; *** p < 0.001.

Journal: MedComm

Article Title: Integrative Single‐Cell Analysis Reveals Targetable Vacuole Membrane Protein 1‐Mediated Mechanism of Tumor Angiogenesis in Glioblastoma

doi: 10.1002/mco2.70619

Figure Lengend Snippet: Overexpression of VMP1 promotes tumor growth. (A) Representative western blot image validating exogenous overexpression of VMP1 in U87 and U251 cell lines. (B) U87 subcutaneous xenografts of overexpressed VMP1 (VMP1‐OE) and control vector at Day 17 post‐injection ( n = 9). (C) The weight of the tumor grafts. (D) Quantification of normalized tumor weights of VMP1‐OE and vector (measured every 4 days from Day 3) ( n = 9). (E) Bioluminescence imaging of U87 VMP1‐OE and vector orthotopic xenografts at different time points from Day 3 to Day 17 ( n = 6). (F) Representative images of hematoxylin and eosin‐stained sections at Day 17 post‐injection. Tumor is indicated within the dashed line. Scale bar, 1000 µm. (G) Relative total photon flux of bioluminescence in mice with U87 VMP1‐OE and vector. (H) Kaplan–Meier survival analysis of mice with U87 VMP1‐OE and vector intracranial xenografts ( n = 6). (I) Top, immunohistochemical staining showing Ki67‐positive cells in subcutaneous and intracranial xenografts. Scale bar, 100 µm. Bottom, quantification of Ki67‐positive cells (%) in subcutaneous and intracranial models. * p < 0.05; ** p < 0.01; *** p < 0.001.

Article Snippet: Human embryonic kidney cells 293T (293T) and human GBM cell lines U87 and U251 were purchased from the American Type Culture Collection (ATCC).

Techniques: Over Expression, Western Blot, Control, Plasmid Preparation, Injection, Imaging, Staining, Immunohistochemical staining

VMP1 promoted tumor growth was independent of autophagy. (A) Representative western blot images of autophagy markers (p62 and LC3 I/II) in U87 and U251 cell lines with VMP1‐OE. (B) Representative transmission electron microscopy images of a cell in U87 and U251 with VMP1‐OE, showing no differences in autophagosome formation. Top: scale bar, 2 µm. Bottom: scale bar, 500 nm. (C) Western blot images of tissue samples from our glioma cohort (glioma) ( n = 47) and normal brain tissue (N), showing the protein expression of autophagy markers (p62, Beclin 1, and LC3 I/II). (D) Quantification of western blot images, patients were separated into two groups based on median VMP1 expression: VMP1 low glioma ( n = 23) and VMP1 high glioma ( n = 24). (E) Correlation analysis of western blot quantification value between VMP1 and autophagy markers (p62, Beclin 1, and LC3 I/II). (F) Confirmation of VMP1 knockdown in U87 and U251 cells using two different targeting sequences by western blot analysis. (G) U87 subcutaneous xenografts of VMP1 knockdown (shVMP1) and control vector (shNC) at Day 27 post‐injection ( n = 10) (left), and the tumor volume measured from Day 14 to Day 27 (right). (H) Bioluminescence imaging of U87 shVMP1 and vector orthotopic xenografts at Day 28. (I) Representative images of hematoxylin and eosin‐stained sections at Day 28 post‐injection. Scale bar, 2000 µm. (J) Kaplan–Meier survival analysis of mice with U87 shVMP1 and vector intracranial xenografts ( n = 5). (K) Representative western blot images of U87 shVMP1 and vector subcutaneous xenografts showing the expression of autophagy markers p62 and LC3 I/II (left). Quantification of band intensities normalized to GAPDH (right). ns, no statistical significance; * p < 0.05; ** p < 0.01; *** p < 0.001.

Journal: MedComm

Article Title: Integrative Single‐Cell Analysis Reveals Targetable Vacuole Membrane Protein 1‐Mediated Mechanism of Tumor Angiogenesis in Glioblastoma

doi: 10.1002/mco2.70619

Figure Lengend Snippet: VMP1 promoted tumor growth was independent of autophagy. (A) Representative western blot images of autophagy markers (p62 and LC3 I/II) in U87 and U251 cell lines with VMP1‐OE. (B) Representative transmission electron microscopy images of a cell in U87 and U251 with VMP1‐OE, showing no differences in autophagosome formation. Top: scale bar, 2 µm. Bottom: scale bar, 500 nm. (C) Western blot images of tissue samples from our glioma cohort (glioma) ( n = 47) and normal brain tissue (N), showing the protein expression of autophagy markers (p62, Beclin 1, and LC3 I/II). (D) Quantification of western blot images, patients were separated into two groups based on median VMP1 expression: VMP1 low glioma ( n = 23) and VMP1 high glioma ( n = 24). (E) Correlation analysis of western blot quantification value between VMP1 and autophagy markers (p62, Beclin 1, and LC3 I/II). (F) Confirmation of VMP1 knockdown in U87 and U251 cells using two different targeting sequences by western blot analysis. (G) U87 subcutaneous xenografts of VMP1 knockdown (shVMP1) and control vector (shNC) at Day 27 post‐injection ( n = 10) (left), and the tumor volume measured from Day 14 to Day 27 (right). (H) Bioluminescence imaging of U87 shVMP1 and vector orthotopic xenografts at Day 28. (I) Representative images of hematoxylin and eosin‐stained sections at Day 28 post‐injection. Scale bar, 2000 µm. (J) Kaplan–Meier survival analysis of mice with U87 shVMP1 and vector intracranial xenografts ( n = 5). (K) Representative western blot images of U87 shVMP1 and vector subcutaneous xenografts showing the expression of autophagy markers p62 and LC3 I/II (left). Quantification of band intensities normalized to GAPDH (right). ns, no statistical significance; * p < 0.05; ** p < 0.01; *** p < 0.001.

Article Snippet: Human embryonic kidney cells 293T (293T) and human GBM cell lines U87 and U251 were purchased from the American Type Culture Collection (ATCC).

Techniques: Western Blot, Transmission Assay, Electron Microscopy, Expressing, Knockdown, Control, Plasmid Preparation, Injection, Imaging, Staining

VMP1 mediates angiogenesis and vascular permeability through activation of endothelial cells in the TME. (A) Representative western blot image (top) and quantification (bottom) of VEGFR2 expression in human primary endothelial cells (HUVEC) after culturing with conditioned medium (CM) collected from VMP1‐overexpressing glioblastoma cell lines U87 and U251. (B) Representative immunofluorescence image of VEGFR2 expression (red) and DAPI (blue) in HUVEC cultured with conditioned medium. Scale bar, 200 µm. (C) Representative immunofluorescence staining image of VE‐cadherin expression (red) and DAPI (blue) in HUVEC with CM. Scale bar, 200 µm. (D) Human protein angiogenesis array showing 55 angiogenesis‐related proteins in the CM collected. (E) Quantification of eight of the angiogenesis‐related proteins, including tissue factor (TF), granulocyte‐macrophage colony stimulating factor (GM‐CSF), macrophage inflammatory protein 1α (MIP1α), Serpin E1, Thrombospondin‐1 (THBS1), Angiogenin, tissue inhibitor of metalloproteinase 1 (TIMP‐1), and VEGF‐C. (F) Spatial distribution of spot degree between VMP1 high cancer cells and endothelial cells in the spatial mRNA dataset. (G–I) Spatial distribution of angiogenesis (G), Serpin E1 (H), and TIMP1 (I) expression in the spatial mRNA dataset. * p < 0.05; ** p < 0.01; *** p <0 .001.

Journal: MedComm

Article Title: Integrative Single‐Cell Analysis Reveals Targetable Vacuole Membrane Protein 1‐Mediated Mechanism of Tumor Angiogenesis in Glioblastoma

doi: 10.1002/mco2.70619

Figure Lengend Snippet: VMP1 mediates angiogenesis and vascular permeability through activation of endothelial cells in the TME. (A) Representative western blot image (top) and quantification (bottom) of VEGFR2 expression in human primary endothelial cells (HUVEC) after culturing with conditioned medium (CM) collected from VMP1‐overexpressing glioblastoma cell lines U87 and U251. (B) Representative immunofluorescence image of VEGFR2 expression (red) and DAPI (blue) in HUVEC cultured with conditioned medium. Scale bar, 200 µm. (C) Representative immunofluorescence staining image of VE‐cadherin expression (red) and DAPI (blue) in HUVEC with CM. Scale bar, 200 µm. (D) Human protein angiogenesis array showing 55 angiogenesis‐related proteins in the CM collected. (E) Quantification of eight of the angiogenesis‐related proteins, including tissue factor (TF), granulocyte‐macrophage colony stimulating factor (GM‐CSF), macrophage inflammatory protein 1α (MIP1α), Serpin E1, Thrombospondin‐1 (THBS1), Angiogenin, tissue inhibitor of metalloproteinase 1 (TIMP‐1), and VEGF‐C. (F) Spatial distribution of spot degree between VMP1 high cancer cells and endothelial cells in the spatial mRNA dataset. (G–I) Spatial distribution of angiogenesis (G), Serpin E1 (H), and TIMP1 (I) expression in the spatial mRNA dataset. * p < 0.05; ** p < 0.01; *** p <0 .001.

Article Snippet: Human embryonic kidney cells 293T (293T) and human GBM cell lines U87 and U251 were purchased from the American Type Culture Collection (ATCC).

Techniques: Permeability, Activation Assay, Western Blot, Expressing, Immunofluorescence, Cell Culture, Staining

Targeted inhibition of VEGFA represses VMP1‐mediated tumor growth. (A) The treatment timeline and bioluminescence detection of mice treated with bevacizumab (BEV) at different time points (Day 3, Day 7, and Day 13). (B) Bioluminescence detection of U87 tumor‐bearing mice treated with BEV and vehicle. (C) Hematoxylin and eosin staining of mice brain, dotted area indicates the tumor region. Scale bar, 1000 µm. (D) Relative total photon flux in orthotopic mice model after treatment. (E) Changes in body weight in mice after treatments. (F) Kaplan–Meier survival of mice after being treated with bevacizumab and vector control. ns, no statistical significance.

Journal: MedComm

Article Title: Integrative Single‐Cell Analysis Reveals Targetable Vacuole Membrane Protein 1‐Mediated Mechanism of Tumor Angiogenesis in Glioblastoma

doi: 10.1002/mco2.70619

Figure Lengend Snippet: Targeted inhibition of VEGFA represses VMP1‐mediated tumor growth. (A) The treatment timeline and bioluminescence detection of mice treated with bevacizumab (BEV) at different time points (Day 3, Day 7, and Day 13). (B) Bioluminescence detection of U87 tumor‐bearing mice treated with BEV and vehicle. (C) Hematoxylin and eosin staining of mice brain, dotted area indicates the tumor region. Scale bar, 1000 µm. (D) Relative total photon flux in orthotopic mice model after treatment. (E) Changes in body weight in mice after treatments. (F) Kaplan–Meier survival of mice after being treated with bevacizumab and vector control. ns, no statistical significance.

Article Snippet: Human embryonic kidney cells 293T (293T) and human GBM cell lines U87 and U251 were purchased from the American Type Culture Collection (ATCC).

Techniques: Inhibition, Staining, Plasmid Preparation, Control

Validation of the prognostic model utilizing the four ERGs constructed from the training dataset. (A) ROC curves for OS in TCGA-LGGGBM datasets. (B) Kaplan–Meier curve of OS in TCGA-LGGGBM datasets. (C) Risk score distribution, survival status, and expression levels of four ERGs in TCGA-LGGGBM datasets. For the validation sets, (D) showcases ROC curves for OS in mRNAseq_325 datasets, while (E) presents the Kaplan–Meier curve of OS in mRNAseq_325 datasets. (F) further details the risk score distribution, survival status, and expression levels of the 4 ERGs in mRNAseq_325 datasets. Lastly, (G) exhibits ROC curves for OS in mRNA-array_301 datasets, followed by (H) , which illustrates the Kaplan–Meier curve of OS in mRNA-array_301 datasets. Finally, (I) depicts the risk score distribution, survival status, and expression levels of the 4 ERGs in mRNA-array_301 datasets. “ERGs” refer to epilepsy-related genes, while “ROC” denotes the receiver operating curve. ERGs, epilepsy-related genes; GBM, glioblastoma; LGG, lower grade glioma; OS, overall survival; ROC: receiver operating characteristic; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: Validation of the prognostic model utilizing the four ERGs constructed from the training dataset. (A) ROC curves for OS in TCGA-LGGGBM datasets. (B) Kaplan–Meier curve of OS in TCGA-LGGGBM datasets. (C) Risk score distribution, survival status, and expression levels of four ERGs in TCGA-LGGGBM datasets. For the validation sets, (D) showcases ROC curves for OS in mRNAseq_325 datasets, while (E) presents the Kaplan–Meier curve of OS in mRNAseq_325 datasets. (F) further details the risk score distribution, survival status, and expression levels of the 4 ERGs in mRNAseq_325 datasets. Lastly, (G) exhibits ROC curves for OS in mRNA-array_301 datasets, followed by (H) , which illustrates the Kaplan–Meier curve of OS in mRNA-array_301 datasets. Finally, (I) depicts the risk score distribution, survival status, and expression levels of the 4 ERGs in mRNA-array_301 datasets. “ERGs” refer to epilepsy-related genes, while “ROC” denotes the receiver operating curve. ERGs, epilepsy-related genes; GBM, glioblastoma; LGG, lower grade glioma; OS, overall survival; ROC: receiver operating characteristic; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Article Snippet: Human glioblastoma (GBM) cell lines U87 and U251 were obtained from the American Type Culture Collection (ATCC) and maintained in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS).

Techniques: Biomarker Discovery, Construct, Expressing

Significance of the ERG risk score as an independent and robust predictive indicator within TCGA-LGGGBM dataset. (A) Results of multivariate Cox regression analyses, showcasing the influence of the risk score alongside clinical–pathological features identified from univariate Cox analysis on OS in the TCGA-LGGGBM dataset. (B) Nomogram incorporating the 4-SRG risk score and age, derived from the TCGA-LGGGBM dataset. By summing the points from these variables, one can determine the total points, projecting onto the bottom scales to ascertain the probability of 1-, 3-, and 5-year OS. (C) Calibration plot for 1-/3-/5-year intervals, validating the accuracy of the prognostic nomogram. (D) Kaplan–Meier curve illustrating OS for the score calculated from the nomogram. ERGs, epilepsy-related genes; GBM, glioblastoma; LGG, lower grade glioma; OS, overall survival; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: Significance of the ERG risk score as an independent and robust predictive indicator within TCGA-LGGGBM dataset. (A) Results of multivariate Cox regression analyses, showcasing the influence of the risk score alongside clinical–pathological features identified from univariate Cox analysis on OS in the TCGA-LGGGBM dataset. (B) Nomogram incorporating the 4-SRG risk score and age, derived from the TCGA-LGGGBM dataset. By summing the points from these variables, one can determine the total points, projecting onto the bottom scales to ascertain the probability of 1-, 3-, and 5-year OS. (C) Calibration plot for 1-/3-/5-year intervals, validating the accuracy of the prognostic nomogram. (D) Kaplan–Meier curve illustrating OS for the score calculated from the nomogram. ERGs, epilepsy-related genes; GBM, glioblastoma; LGG, lower grade glioma; OS, overall survival; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Techniques: Derivative Assay

Relationship between ERG risk score and immune cell infiltration, as well as the expression of immune checkpoint genes. (A) Heatmap visualizing the correlation between risk score and immune cell infiltration, calculated with the XCELL algorithm using data across the TCGA-LGGGBM dataset and two CGGA datasets. Scatter plots illustrate the correlation between the risk score and the scores of microenvironment (B) , immune (C) , and stroma (D) , as well as the correlation between the risk score and the infiltration of phagocytes (E) . (F) Heatmap presenting the correlation between the risk score and the expression of immune checkpoint genes across the same datasets. Scatter plots demonstrate the correlation between the risk score and the expression of specific immune checkpoint genes, such as SIGLEC7 (G) and PDCD1 (H) . CGGA, Chinese Glioma Genome Atlas; ERG, epilepsy-related gene; GBM, glioblastoma; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: Relationship between ERG risk score and immune cell infiltration, as well as the expression of immune checkpoint genes. (A) Heatmap visualizing the correlation between risk score and immune cell infiltration, calculated with the XCELL algorithm using data across the TCGA-LGGGBM dataset and two CGGA datasets. Scatter plots illustrate the correlation between the risk score and the scores of microenvironment (B) , immune (C) , and stroma (D) , as well as the correlation between the risk score and the infiltration of phagocytes (E) . (F) Heatmap presenting the correlation between the risk score and the expression of immune checkpoint genes across the same datasets. Scatter plots demonstrate the correlation between the risk score and the expression of specific immune checkpoint genes, such as SIGLEC7 (G) and PDCD1 (H) . CGGA, Chinese Glioma Genome Atlas; ERG, epilepsy-related gene; GBM, glioblastoma; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Techniques: Expressing

The intricate connections between ERG risk score and the imputed sensitivity of anti-tumor drugs and oncogene expressions. (A) Heatmap visualizing the correlation between the risk score and the imputed sensitivity of anti-tumor drugs across the TCGA-LGGGBM dataset and two CGGA datasets. (B) Scatter plots offering a focused view of the correlation between the risk score and the sensitivity of vorinostat, a representative anti-tumor drug. (C–E) Volcano plots depicting correlation analyses between the risk score and the expression of oncogenes across the TCGA-LGGGBM, mRNAseq_325, and mRNA-array_301 datasets, highlighting significant associations. (F) Heatmap presenting the correlation between the risk score and the expression of oncogenes across the TCGA-LGGGBM dataset and two CGGA datasets. CGGA, Chinese Glioma Genome Atlas; ERG, epilepsy-related gene; GBM, glioblastoma; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: The intricate connections between ERG risk score and the imputed sensitivity of anti-tumor drugs and oncogene expressions. (A) Heatmap visualizing the correlation between the risk score and the imputed sensitivity of anti-tumor drugs across the TCGA-LGGGBM dataset and two CGGA datasets. (B) Scatter plots offering a focused view of the correlation between the risk score and the sensitivity of vorinostat, a representative anti-tumor drug. (C–E) Volcano plots depicting correlation analyses between the risk score and the expression of oncogenes across the TCGA-LGGGBM, mRNAseq_325, and mRNA-array_301 datasets, highlighting significant associations. (F) Heatmap presenting the correlation between the risk score and the expression of oncogenes across the TCGA-LGGGBM dataset and two CGGA datasets. CGGA, Chinese Glioma Genome Atlas; ERG, epilepsy-related gene; GBM, glioblastoma; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Techniques: Expressing

Correlation of the ERG risk score with cancer progression. (A) Heatmap illustrating the outcomes of GSEA analysis concerning the biological processes associated with the risk score, utilizing data from TCGA-LGGGBM and two CGGA datasets. (B) GSEA plots demonstrating the relationship between risk score and several immune functions such as mononuclear cell differentiation, leukocyte-mediated immunity, and lymphocyte-mediated immunity. (C) Heatmap presenting the results of GSEA analysis for KEGG pathways associated with the risk score, using data from TCGA-LGGGBM and two CGGA datasets. (D) GSEA plots depicting the association between the risk score and DNA replication, cell cycle, and focal adhesion. Lollipop plots represent the findings of enrichment analysis regarding differentially expressed genes in both biological processes (E) and KEGG pathways (F) between high- and low-risk groups. CGGA, Chinese Glioma Genome Atlas; ERGs, epilepsy-related genes; GBM, glioblastoma; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: Correlation of the ERG risk score with cancer progression. (A) Heatmap illustrating the outcomes of GSEA analysis concerning the biological processes associated with the risk score, utilizing data from TCGA-LGGGBM and two CGGA datasets. (B) GSEA plots demonstrating the relationship between risk score and several immune functions such as mononuclear cell differentiation, leukocyte-mediated immunity, and lymphocyte-mediated immunity. (C) Heatmap presenting the results of GSEA analysis for KEGG pathways associated with the risk score, using data from TCGA-LGGGBM and two CGGA datasets. (D) GSEA plots depicting the association between the risk score and DNA replication, cell cycle, and focal adhesion. Lollipop plots represent the findings of enrichment analysis regarding differentially expressed genes in both biological processes (E) and KEGG pathways (F) between high- and low-risk groups. CGGA, Chinese Glioma Genome Atlas; ERGs, epilepsy-related genes; GBM, glioblastoma; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; LGG, lower grade glioma; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Techniques: Cell Differentiation

PAX3 expression in glioma and its implications in cancer progression. (A) Heatmap illustrating the correlation between ERG risk score and the expression of ERGs within the model. Violin plots displaying the differential expression of PAX3 in TCGA-LGGGBM (B) , GSE4290 (C) , and GSE50161 (D) datasets, as well as OS analysis results regarding PAX3 (E) , DSS (F) , and PFI (G) in TCGA-LGGGBM datasets. (H) Volcano plot showcasing the correlation between PAX3 expression and oncogenes. (I) Lollipop plot presenting the correlation between PAX3 expression and immune checkpoint genes. (J) Lollipop plot demonstrating the correlation between PAX3 expression and the infiltration of multiple immune and stroma cells. GSEA plots highlighting the association of PAX3 with various biological processes (K, L) and KEGG pathways (M) in the TCGA-LGGGBM dataset. (N) Scatter plot depicting PAX3 expression correlated with the imputed sensitivity score of vorinostat. DSS, disease-specific survival; ERGs, epilepsy-related genes; GBM, glioblastoma; GSEA, gene set enrichment analysis; LGG, lower grade glioma; GBM, Glioblastoma; LGG, lower grade glioma; KEGG, Kyoto Encyclopedia of Genes and Genomes; PFI, progression-free interval; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: PAX3 expression in glioma and its implications in cancer progression. (A) Heatmap illustrating the correlation between ERG risk score and the expression of ERGs within the model. Violin plots displaying the differential expression of PAX3 in TCGA-LGGGBM (B) , GSE4290 (C) , and GSE50161 (D) datasets, as well as OS analysis results regarding PAX3 (E) , DSS (F) , and PFI (G) in TCGA-LGGGBM datasets. (H) Volcano plot showcasing the correlation between PAX3 expression and oncogenes. (I) Lollipop plot presenting the correlation between PAX3 expression and immune checkpoint genes. (J) Lollipop plot demonstrating the correlation between PAX3 expression and the infiltration of multiple immune and stroma cells. GSEA plots highlighting the association of PAX3 with various biological processes (K, L) and KEGG pathways (M) in the TCGA-LGGGBM dataset. (N) Scatter plot depicting PAX3 expression correlated with the imputed sensitivity score of vorinostat. DSS, disease-specific survival; ERGs, epilepsy-related genes; GBM, glioblastoma; GSEA, gene set enrichment analysis; LGG, lower grade glioma; GBM, Glioblastoma; LGG, lower grade glioma; KEGG, Kyoto Encyclopedia of Genes and Genomes; PFI, progression-free interval; TCGA-LGGGBM, The Cancer Genome Atlas Merged Cohort of LGG and GBM.

Techniques: Expressing, Quantitative Proteomics

Impact of PAX3 on glioma cell dynamics and vorinostat sensitivity. (A–D) CCK-8 assay evaluating the effects of PAX3 knockdown or overexpression on U251 and U87 cell proliferation. (E) Images representing cell proliferation in the EdU assay and (F) corresponding quantitative analysis exhibiting changes in cell proliferation of glioma cells upon knockdown or overexpression of PAX3 . (G) Images representing migration status in transwell cell migration assay and (H) corresponding quantitative analysis exhibiting changes in migration ability of glioma cells upon knockdown or overexpression of PAX3 . (I, J) Quantitative findings delineating alterations in glioma cell migratory capacity following PAX3 knockdown in the wound healing assay. (K) Images representing expression of E-cadherin and CDH2 in glioma cells by western blot analysis and (L, M) corresponding quantitative analysis results demonstrating alterations in their expression in glioma cells following PAX3 knockdown or overexpression. (N) Heatmap depicting the association between PAX3 expression levels and HDACs . (O, P) CCK-8 assay measuring the IC50 of vorinostat in glioma cells with PAX3 knockdown or overexpression. CCK-8, Cell Counting Kit-8; CDH1 , E-cadherin; CDH2 , N-cadherin; IC50, half-maximal inhibitory concentration.

Journal: Frontiers in Neurology

Article Title: Prognostic modeling of glioma using epilepsy-related genes highlights PAX3 as a regulator of migration and vorinostat sensitivity

doi: 10.3389/fneur.2025.1665835

Figure Lengend Snippet: Impact of PAX3 on glioma cell dynamics and vorinostat sensitivity. (A–D) CCK-8 assay evaluating the effects of PAX3 knockdown or overexpression on U251 and U87 cell proliferation. (E) Images representing cell proliferation in the EdU assay and (F) corresponding quantitative analysis exhibiting changes in cell proliferation of glioma cells upon knockdown or overexpression of PAX3 . (G) Images representing migration status in transwell cell migration assay and (H) corresponding quantitative analysis exhibiting changes in migration ability of glioma cells upon knockdown or overexpression of PAX3 . (I, J) Quantitative findings delineating alterations in glioma cell migratory capacity following PAX3 knockdown in the wound healing assay. (K) Images representing expression of E-cadherin and CDH2 in glioma cells by western blot analysis and (L, M) corresponding quantitative analysis results demonstrating alterations in their expression in glioma cells following PAX3 knockdown or overexpression. (N) Heatmap depicting the association between PAX3 expression levels and HDACs . (O, P) CCK-8 assay measuring the IC50 of vorinostat in glioma cells with PAX3 knockdown or overexpression. CCK-8, Cell Counting Kit-8; CDH1 , E-cadherin; CDH2 , N-cadherin; IC50, half-maximal inhibitory concentration.

Techniques: CCK-8 Assay, Knockdown, Over Expression, EdU Assay, Migration, Cell Migration Assay, Wound Healing Assay, Expressing, Western Blot, Cell Counting, Concentration Assay

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