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Proteintech utp20

Construction of a Ribosome biogenesis-related gene signature based on the TCGA glioma cohort. (A) Volcano map displayed the genes that affected the survival of glioma patients. (B) Venn diagram showing the intersection of 331 RBRGs with risky genes from TCGA. (C) LASSO regression analysis narrowing down to 22 candidate genes. (D) Univariate and (E) multivariate Cox regression selecting 4 independent prognostic genes. (F) Genomic map showing specific localization of NOP10, <t>UTP20,</t> SHQ1, and PIH1D2 in chromosomes. (G) Circos plot showing the correlation among these 4 genes. (H) Kaplan–Meier survival analysis of glioma patients in the RBRGs-high and -low groups using the TCGA cohort. (I) Scatter plot demonstrating survival time and number of deaths in the two groups. (J) Time-dependent ROC curves demonstrating the predictive accuracy of RBRGs for 1-, 3-, and 5-year survival in glioma patients.

Utp20, supplied by Proteintech, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 93 stars, based on 1 article reviews

utp20 - by Bioz Stars, 2026-05

93/100 stars

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1) Product Images from "Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target"

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

Journal: Frontiers in Immunology

doi: 10.3389/fimmu.2025.1680667

Figure Legend Snippet: Construction of a Ribosome biogenesis-related gene signature based on the TCGA glioma cohort. (A) Volcano map displayed the genes that affected the survival of glioma patients. (B) Venn diagram showing the intersection of 331 RBRGs with risky genes from TCGA. (C) LASSO regression analysis narrowing down to 22 candidate genes. (D) Univariate and (E) multivariate Cox regression selecting 4 independent prognostic genes. (F) Genomic map showing specific localization of NOP10, UTP20, SHQ1, and PIH1D2 in chromosomes. (G) Circos plot showing the correlation among these 4 genes. (H) Kaplan–Meier survival analysis of glioma patients in the RBRGs-high and -low groups using the TCGA cohort. (I) Scatter plot demonstrating survival time and number of deaths in the two groups. (J) Time-dependent ROC curves demonstrating the predictive accuracy of RBRGs for 1-, 3-, and 5-year survival in glioma patients.

Techniques Used:

Validation of RBRGs expression levels and prognostic value was based on multiple cohorts. (A, B) TCGA and GSE16011 cohorts were used to validate the expression levels of NOP10, UTP20, SHQ1, PIH1D2, and RBRGs in normal and glioma tissues, respectively. (C–E) The Kaplan-Meier curves, scatter plots, and time-dependent ROC curves were utilized to validate the prognostic value of RBRGs in glioma using the CGGA301, CGGA325, and GSE43378 cohorts. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Figure Legend Snippet: Validation of RBRGs expression levels and prognostic value was based on multiple cohorts. (A, B) TCGA and GSE16011 cohorts were used to validate the expression levels of NOP10, UTP20, SHQ1, PIH1D2, and RBRGs in normal and glioma tissues, respectively. (C–E) The Kaplan-Meier curves, scatter plots, and time-dependent ROC curves were utilized to validate the prognostic value of RBRGs in glioma using the CGGA301, CGGA325, and GSE43378 cohorts. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Techniques Used: Biomarker Discovery, Expressing

Role of RBRGs in the tumor microenvironment. (A) Differences in infiltration of immune cells between RBRGs-high and -low groups based on the CIBERSORTx algorithm. (B) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with immune cells. (C–E) Differences in MDSC, CAF, and ESTIMATE scores between RBRGs-high and -low groups. (F) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with ESTIMATE score. (G) Differences in immune subtypes between RBRGs-high and -low groups. (H) Kaplan-Meier curve showing the effect of immune subtype on overall survival of glioma patients. C1: wound healing, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet. (I, J) Single-cell analysis demonstrating the expression levels of NOP10, UTP20, SHQ1, and PIH1D2 in different cells based on the GSE131928 cohort. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure Legend Snippet: Role of RBRGs in the tumor microenvironment. (A) Differences in infiltration of immune cells between RBRGs-high and -low groups based on the CIBERSORTx algorithm. (B) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with immune cells. (C–E) Differences in MDSC, CAF, and ESTIMATE scores between RBRGs-high and -low groups. (F) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with ESTIMATE score. (G) Differences in immune subtypes between RBRGs-high and -low groups. (H) Kaplan-Meier curve showing the effect of immune subtype on overall survival of glioma patients. C1: wound healing, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet. (I, J) Single-cell analysis demonstrating the expression levels of NOP10, UTP20, SHQ1, and PIH1D2 in different cells based on the GSE131928 cohort. *p < 0.05, **p < 0.01, ***p < 0.001.

Techniques Used: Single-cell Analysis, Expressing

RBRGs predicted the efficacy of immunotherapy. Differences in cancer-immunity cycle between patients in the RBRGs-high and -low groups were based on the TCGA glioma cohort. (A) Step 1: release of cancer cell antigens. (B) Step 2: cancer antigen presentation. (C) Step 3: priming and activation. (D) Step 4: trafficking of immune cells to tumors. (E) Step 5: infiltration of immune cells into tumors. (F) Step 6: recognition of cancer cells by T cells. (G) Step7: killing of cancer cells. (H) Differential expression of immunosuppressive checkpoints in RBRGs-high and -low groups. (I) Heatmap demonstrating the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with multiple immunosuppressive checkpoints. (J-M) Kaplan-Meier curves demonstrating RBRGs combined with CD274, PDCD1, PDCD1LG2, or TNFRSF18 respectively, to predict overall survival in glioma patients. (N) Differences in TIDE scores between RBRGs-high and -low groups. (O, P) Differences in survival between patients in the RBRGs-high and -low groups receiving immunotherapy were analyzed based on the glioma PRJNA482620 and melanoma GSE91061 cohorts. *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Figure Legend Snippet: RBRGs predicted the efficacy of immunotherapy. Differences in cancer-immunity cycle between patients in the RBRGs-high and -low groups were based on the TCGA glioma cohort. (A) Step 1: release of cancer cell antigens. (B) Step 2: cancer antigen presentation. (C) Step 3: priming and activation. (D) Step 4: trafficking of immune cells to tumors. (E) Step 5: infiltration of immune cells into tumors. (F) Step 6: recognition of cancer cells by T cells. (G) Step7: killing of cancer cells. (H) Differential expression of immunosuppressive checkpoints in RBRGs-high and -low groups. (I) Heatmap demonstrating the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with multiple immunosuppressive checkpoints. (J-M) Kaplan-Meier curves demonstrating RBRGs combined with CD274, PDCD1, PDCD1LG2, or TNFRSF18 respectively, to predict overall survival in glioma patients. (N) Differences in TIDE scores between RBRGs-high and -low groups. (O, P) Differences in survival between patients in the RBRGs-high and -low groups receiving immunotherapy were analyzed based on the glioma PRJNA482620 and melanoma GSE91061 cohorts. *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Techniques Used: Immunopeptidomics, Activation Assay, Quantitative Proteomics

Genetic mutations. (A) The oncoplot depicted genetic mutations differences in glioma patients between RBRGs-high and -low groups. (B) Kaplan-Meier curves demonstratingthe difference in overall survival of glioma patients between the EGFR, NF1 and PTEN mutation and wild-type groups. (C) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between EGFR, NF1and PTEN mutant and wild-type groups, respectively. (D) Kaplan-Meier curves demonstrating the difference in overall survival of glioma patients between the IDH1, CIC and ATRX mutation and wild-type groups. (E) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between IDH1, CIC and ATRX mutant and wild-type groups, respectively.*p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Figure Legend Snippet: Genetic mutations. (A) The oncoplot depicted genetic mutations differences in glioma patients between RBRGs-high and -low groups. (B) Kaplan-Meier curves demonstratingthe difference in overall survival of glioma patients between the EGFR, NF1 and PTEN mutation and wild-type groups. (C) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between EGFR, NF1and PTEN mutant and wild-type groups, respectively. (D) Kaplan-Meier curves demonstrating the difference in overall survival of glioma patients between the IDH1, CIC and ATRX mutation and wild-type groups. (E) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between IDH1, CIC and ATRX mutant and wild-type groups, respectively.*p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Techniques Used: Mutagenesis, Quantitative Proteomics

Knockdown of UTP20 expression inhibited proliferation and invasion of glioma U251 and U87 cells in vitro . (A) UTP20 mRNA levels in U87 and U251 cells after knockdown. (B) Western Blot showing UTP20 protein levels in U87 and U251 cells after knockdown. (C, D) MTS assay for cell proliferation in U87 and U251 after UTP20 knockdown. (E) Colony formation assay showing the number of colonies in U87 and U251 after UTP20 knockdown. (F) Quantification of colonies formed in U87 and U251 cells after UTP20 knockdown. (G) Transwell invasion assay showing cell migration in U87 and U251 cells. (H) Quantification of cell migration in U87 and U251 cells. ***p < 0.001. One-way ANOVA with Tukey’s test for (A, F) , and (H) Two-way ANOVA with Tukey’s test for (C, D) .

Figure Legend Snippet: Knockdown of UTP20 expression inhibited proliferation and invasion of glioma U251 and U87 cells in vitro . (A) UTP20 mRNA levels in U87 and U251 cells after knockdown. (B) Western Blot showing UTP20 protein levels in U87 and U251 cells after knockdown. (C, D) MTS assay for cell proliferation in U87 and U251 after UTP20 knockdown. (E) Colony formation assay showing the number of colonies in U87 and U251 after UTP20 knockdown. (F) Quantification of colonies formed in U87 and U251 cells after UTP20 knockdown. (G) Transwell invasion assay showing cell migration in U87 and U251 cells. (H) Quantification of cell migration in U87 and U251 cells. ***p < 0.001. One-way ANOVA with Tukey’s test for (A, F) , and (H) Two-way ANOVA with Tukey’s test for (C, D) .

Techniques Used: Knockdown, Expressing, In Vitro, Western Blot, MTS Assay, Colony Assay, Transwell Invasion Assay, Migration

Image Search Results

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: Construction of a Ribosome biogenesis-related gene signature based on the TCGA glioma cohort. (A) Volcano map displayed the genes that affected the survival of glioma patients. (B) Venn diagram showing the intersection of 331 RBRGs with risky genes from TCGA. (C) LASSO regression analysis narrowing down to 22 candidate genes. (D) Univariate and (E) multivariate Cox regression selecting 4 independent prognostic genes. (F) Genomic map showing specific localization of NOP10, UTP20, SHQ1, and PIH1D2 in chromosomes. (G) Circos plot showing the correlation among these 4 genes. (H) Kaplan–Meier survival analysis of glioma patients in the RBRGs-high and -low groups using the TCGA cohort. (I) Scatter plot demonstrating survival time and number of deaths in the two groups. (J) Time-dependent ROC curves demonstrating the predictive accuracy of RBRGs for 1-, 3-, and 5-year survival in glioma patients.

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques:

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: Validation of RBRGs expression levels and prognostic value was based on multiple cohorts. (A, B) TCGA and GSE16011 cohorts were used to validate the expression levels of NOP10, UTP20, SHQ1, PIH1D2, and RBRGs in normal and glioma tissues, respectively. (C–E) The Kaplan-Meier curves, scatter plots, and time-dependent ROC curves were utilized to validate the prognostic value of RBRGs in glioma using the CGGA301, CGGA325, and GSE43378 cohorts. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques: Biomarker Discovery, Expressing

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: Role of RBRGs in the tumor microenvironment. (A) Differences in infiltration of immune cells between RBRGs-high and -low groups based on the CIBERSORTx algorithm. (B) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with immune cells. (C–E) Differences in MDSC, CAF, and ESTIMATE scores between RBRGs-high and -low groups. (F) Heatmap showing the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with ESTIMATE score. (G) Differences in immune subtypes between RBRGs-high and -low groups. (H) Kaplan-Meier curve showing the effect of immune subtype on overall survival of glioma patients. C1: wound healing, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet. (I, J) Single-cell analysis demonstrating the expression levels of NOP10, UTP20, SHQ1, and PIH1D2 in different cells based on the GSE131928 cohort. *p < 0.05, **p < 0.01, ***p < 0.001.

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques: Single-cell Analysis, Expressing

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: RBRGs predicted the efficacy of immunotherapy. Differences in cancer-immunity cycle between patients in the RBRGs-high and -low groups were based on the TCGA glioma cohort. (A) Step 1: release of cancer cell antigens. (B) Step 2: cancer antigen presentation. (C) Step 3: priming and activation. (D) Step 4: trafficking of immune cells to tumors. (E) Step 5: infiltration of immune cells into tumors. (F) Step 6: recognition of cancer cells by T cells. (G) Step7: killing of cancer cells. (H) Differential expression of immunosuppressive checkpoints in RBRGs-high and -low groups. (I) Heatmap demonstrating the correlation of NOP10, UTP20, SHQ1, and PIH1D2 with multiple immunosuppressive checkpoints. (J-M) Kaplan-Meier curves demonstrating RBRGs combined with CD274, PDCD1, PDCD1LG2, or TNFRSF18 respectively, to predict overall survival in glioma patients. (N) Differences in TIDE scores between RBRGs-high and -low groups. (O, P) Differences in survival between patients in the RBRGs-high and -low groups receiving immunotherapy were analyzed based on the glioma PRJNA482620 and melanoma GSE91061 cohorts. *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques: Immunopeptidomics, Activation Assay, Quantitative Proteomics

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: Genetic mutations. (A) The oncoplot depicted genetic mutations differences in glioma patients between RBRGs-high and -low groups. (B) Kaplan-Meier curves demonstratingthe difference in overall survival of glioma patients between the EGFR, NF1 and PTEN mutation and wild-type groups. (C) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between EGFR, NF1and PTEN mutant and wild-type groups, respectively. (D) Kaplan-Meier curves demonstrating the difference in overall survival of glioma patients between the IDH1, CIC and ATRX mutation and wild-type groups. (E) Differential expression of NOP10, UTP20, SHQ1, and PIH1D2 between IDH1, CIC and ATRX mutant and wild-type groups, respectively.*p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques: Mutagenesis, Quantitative Proteomics

Journal: Frontiers in Immunology

Article Title: Ribosome biogenesis-related gene signature predicts prognosis and immune landscape in glioma and identifies UTP20 as a therapeutic target

doi: 10.3389/fimmu.2025.1680667

Figure Lengend Snippet: Knockdown of UTP20 expression inhibited proliferation and invasion of glioma U251 and U87 cells in vitro . (A) UTP20 mRNA levels in U87 and U251 cells after knockdown. (B) Western Blot showing UTP20 protein levels in U87 and U251 cells after knockdown. (C, D) MTS assay for cell proliferation in U87 and U251 after UTP20 knockdown. (E) Colony formation assay showing the number of colonies in U87 and U251 after UTP20 knockdown. (F) Quantification of colonies formed in U87 and U251 cells after UTP20 knockdown. (G) Transwell invasion assay showing cell migration in U87 and U251 cells. (H) Quantification of cell migration in U87 and U251 cells. ***p < 0.001. One-way ANOVA with Tukey’s test for (A, F) , and (H) Two-way ANOVA with Tukey’s test for (C, D) .

Article Snippet: The membranes were blocked with 5% skimmed milk and then incubated overnight at 4 °C with primary antibodies: UTP20 (Proteintech, 18830-1-AP) and β-actin (Abmart, P30002 ).

Techniques: Knockdown, Expressing, In Vitro, Western Blot, MTS Assay, Colony Assay, Transwell Invasion Assay, Migration

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