Review



mouse ccrcc cell line renca  (ATCC)


Bioz Verified Symbol ATCC is a verified supplier
Bioz Manufacturer Symbol ATCC manufactures this product  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 96

    Structured Review

    ATCC mouse ccrcc cell line renca
    The process of identifying differentially expressed IMRGs and molecular subtypes in <t>ccRCC:</t> ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.
    Mouse Ccrcc Cell Line Renca, supplied by ATCC, used in various techniques. Bioz Stars score: 96/100, based on 524 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mouse ccrcc cell line renca/product/ATCC
    Average 96 stars, based on 524 article reviews
    mouse ccrcc cell line renca - by Bioz Stars, 2026-05
    96/100 stars

    Images

    1) Product Images from "An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies"

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    Journal: Cancers

    doi: 10.3390/cancers18091373

    The process of identifying differentially expressed IMRGs and molecular subtypes in ccRCC: ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.
    Figure Legend Snippet: The process of identifying differentially expressed IMRGs and molecular subtypes in ccRCC: ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.

    Techniques Used: Generated, Gene Expression, Comparison

    Comparison of genomic alteration landscapes between the two molecular subtypes. ( A ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 1. ( B ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 2. ( C ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 1. ( D ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 2. ( E ) The boxplot illustrates the distinct tumor mutation frequencies between Cluster 1 and Cluster 2. ( F ) The Kaplan–Meier curve shows the overall survival rates of patients with high and low tumor mutation burdens. ( G ) Multivariate Cox regression analysis of tumor mutation burden (TMB) and immunometabolic clusters. ( H ) Kaplan–Meier survival curves for ccRCC patients stratified by both TMB status (high vs. low) and immunometabolic clusters (C1 vs. C2).
    Figure Legend Snippet: Comparison of genomic alteration landscapes between the two molecular subtypes. ( A ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 1. ( B ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 2. ( C ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 1. ( D ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 2. ( E ) The boxplot illustrates the distinct tumor mutation frequencies between Cluster 1 and Cluster 2. ( F ) The Kaplan–Meier curve shows the overall survival rates of patients with high and low tumor mutation burdens. ( G ) Multivariate Cox regression analysis of tumor mutation burden (TMB) and immunometabolic clusters. ( H ) Kaplan–Meier survival curves for ccRCC patients stratified by both TMB status (high vs. low) and immunometabolic clusters (C1 vs. C2).

    Techniques Used: Comparison, Mutagenesis

    Assessment and confirmation of the predictive performance of the signature in ccRCC. ( A – C ) Scatter plots illustrating the survival status and IMI scores of ccRCC patients in the TCGA training group ( A ), the TCGA testing group ( B ), and the E-MATB-1980 external validation group ( C ). ( D – F ) Kaplan–Meier curves displaying the overall survival situation per IMI scores of the high-IMI group and low-IMI group in the TCGA training group ( D ), the TCGA testing group ( E ), and the E-MATB-1980 external validation group ( F ). ( G – I ) ROC curves demonstrating the predictive performance of IMI with AUC values for 1-year, 3-year, and 5-year OS in ccRCC patients from the TCGA training group ( G ), the TCGA testing group ( H ), and the E-MATB-1980 external validation group ( I ).
    Figure Legend Snippet: Assessment and confirmation of the predictive performance of the signature in ccRCC. ( A – C ) Scatter plots illustrating the survival status and IMI scores of ccRCC patients in the TCGA training group ( A ), the TCGA testing group ( B ), and the E-MATB-1980 external validation group ( C ). ( D – F ) Kaplan–Meier curves displaying the overall survival situation per IMI scores of the high-IMI group and low-IMI group in the TCGA training group ( D ), the TCGA testing group ( E ), and the E-MATB-1980 external validation group ( F ). ( G – I ) ROC curves demonstrating the predictive performance of IMI with AUC values for 1-year, 3-year, and 5-year OS in ccRCC patients from the TCGA training group ( G ), the TCGA testing group ( H ), and the E-MATB-1980 external validation group ( I ).

    Techniques Used: Biomarker Discovery

    Identification of expression trends of nine IMRGs. ( A ) Differences in signature gene expression between high and low IMI groups in the TCGA database. ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. ( B ) Differences in signature gene expression between normal kidney tissue samples and ccRCC samples in the TCGA database. ( C – K ) The relative expression levels of signature genes between three ccRCC cell lines (786-O, A498, ACHN) and normal renal tubular epithelial cells, HK2. ( L ) The IHC images compared the expression levels of four signature genes between normal renal tissue samples and ccRCC samples in the HPA database ( https://www.proteinatlas.org , accessed on 1 January 2024).
    Figure Legend Snippet: Identification of expression trends of nine IMRGs. ( A ) Differences in signature gene expression between high and low IMI groups in the TCGA database. ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. ( B ) Differences in signature gene expression between normal kidney tissue samples and ccRCC samples in the TCGA database. ( C – K ) The relative expression levels of signature genes between three ccRCC cell lines (786-O, A498, ACHN) and normal renal tubular epithelial cells, HK2. ( L ) The IHC images compared the expression levels of four signature genes between normal renal tissue samples and ccRCC samples in the HPA database ( https://www.proteinatlas.org , accessed on 1 January 2024).

    Techniques Used: Expressing, Gene Expression

    Verification of UCN promoting proliferation, migration, and invasion of ccRCC. ( A ) Knockdown of the UCN gene in 786-O and ACHN cells, relative mRNA levels in the negative control (NC) group and three siRNA knockdown groups, respectively. **** p < 0.0001 ( B ) The knockdown effect of three siRNAs on the UCN gene at the protein level in two cell lines. The uncropped blots are shown in . ( C ) The proliferation curves of CCK8 in the control group and the knockdown groups of the two cell lines. Any siRNA group has significant statistical differences from the NC group. ( D , E ) Wound-healing assays in control and knockdown groups of the two cell lines. ( F , G ) Transwell invasion assays in control and knockdown groups of the two cell lines.
    Figure Legend Snippet: Verification of UCN promoting proliferation, migration, and invasion of ccRCC. ( A ) Knockdown of the UCN gene in 786-O and ACHN cells, relative mRNA levels in the negative control (NC) group and three siRNA knockdown groups, respectively. **** p < 0.0001 ( B ) The knockdown effect of three siRNAs on the UCN gene at the protein level in two cell lines. The uncropped blots are shown in . ( C ) The proliferation curves of CCK8 in the control group and the knockdown groups of the two cell lines. Any siRNA group has significant statistical differences from the NC group. ( D , E ) Wound-healing assays in control and knockdown groups of the two cell lines. ( F , G ) Transwell invasion assays in control and knockdown groups of the two cell lines.

    Techniques Used: Migration, Knockdown, Negative Control, Control

    UCN regulates the immune microenvironment and promotes ccRCC progression. ( A ) Schematic illustration of the mouse xenograft tumor model experimental design. ( B – E ) Tumor growth analyses demonstrate reduced tumor volume and weight across different experimental groups, with notable suppression in sh UCN +IgG2a and sh UCN +PD-1 groups. ( F ) Gating strategy for tumor-infiltrating lymphocytes. Representative flow plots showing the identification of Live/CD45+ cells, T cells (CD3+), CD4+ and CD8+ subsets, as well as Tregs and PD-1+ cells. ( G ) Flow cytometry analysis unveils substantial alterations in immune cell subsets in the tumor immune microenvironment. ( H , I ) Representative mIHC staining of tumors (green: CD8, red: Foxp3, blue: DAPI; scale bar, 50 μm.) ( I ) The column diagram showing the counts of spots with CD8+ T cells and Tregs in tumor slides. Data presented as Mean ± SEM. One-way ANOVA was used in ( E , G , I ). * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.
    Figure Legend Snippet: UCN regulates the immune microenvironment and promotes ccRCC progression. ( A ) Schematic illustration of the mouse xenograft tumor model experimental design. ( B – E ) Tumor growth analyses demonstrate reduced tumor volume and weight across different experimental groups, with notable suppression in sh UCN +IgG2a and sh UCN +PD-1 groups. ( F ) Gating strategy for tumor-infiltrating lymphocytes. Representative flow plots showing the identification of Live/CD45+ cells, T cells (CD3+), CD4+ and CD8+ subsets, as well as Tregs and PD-1+ cells. ( G ) Flow cytometry analysis unveils substantial alterations in immune cell subsets in the tumor immune microenvironment. ( H , I ) Representative mIHC staining of tumors (green: CD8, red: Foxp3, blue: DAPI; scale bar, 50 μm.) ( I ) The column diagram showing the counts of spots with CD8+ T cells and Tregs in tumor slides. Data presented as Mean ± SEM. One-way ANOVA was used in ( E , G , I ). * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.

    Techniques Used: Flow Cytometry, Staining



    Similar Products

    96
    ATCC mouse ccrcc cell line renca
    The process of identifying differentially expressed IMRGs and molecular subtypes in <t>ccRCC:</t> ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.
    Mouse Ccrcc Cell Line Renca, supplied by ATCC, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mouse ccrcc cell line renca/product/ATCC
    Average 96 stars, based on 1 article reviews
    mouse ccrcc cell line renca - by Bioz Stars, 2026-05
    96/100 stars
      Buy from Supplier

    Image Search Results


    The process of identifying differentially expressed IMRGs and molecular subtypes in ccRCC: ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: The process of identifying differentially expressed IMRGs and molecular subtypes in ccRCC: ( A ) IMRGs that were differentially expressed were denoted by red dots for upregulation and blue dots for downregulation. ( B ) Heatmaps were used to visually represent the top differentially expressed genes. ( C ) A heatmap of the nsNMF consensus matrix was generated to classify ccRCC into two molecular subtypes. ( D ) A PCA plot was applied to show significant differences between clusters. ( E ) The gene expression heatmap shows how the identified IMRGs were expressed across the two molecular subtypes. ( F , G ) In order to make a comparison between the two molecular subtypes, the researcher employed the Kaplan–Meier curve to assess and contrast the OS and PFS.

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Generated, Gene Expression, Comparison

    Comparison of genomic alteration landscapes between the two molecular subtypes. ( A ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 1. ( B ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 2. ( C ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 1. ( D ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 2. ( E ) The boxplot illustrates the distinct tumor mutation frequencies between Cluster 1 and Cluster 2. ( F ) The Kaplan–Meier curve shows the overall survival rates of patients with high and low tumor mutation burdens. ( G ) Multivariate Cox regression analysis of tumor mutation burden (TMB) and immunometabolic clusters. ( H ) Kaplan–Meier survival curves for ccRCC patients stratified by both TMB status (high vs. low) and immunometabolic clusters (C1 vs. C2).

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: Comparison of genomic alteration landscapes between the two molecular subtypes. ( A ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 1. ( B ) Oncoplot demonstrated the 30 most frequently mutated genes in Cluster 2. ( C ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 1. ( D ) Heatmap illustrating the co-mutated states of the commonly mutated genes in Cluster 2. ( E ) The boxplot illustrates the distinct tumor mutation frequencies between Cluster 1 and Cluster 2. ( F ) The Kaplan–Meier curve shows the overall survival rates of patients with high and low tumor mutation burdens. ( G ) Multivariate Cox regression analysis of tumor mutation burden (TMB) and immunometabolic clusters. ( H ) Kaplan–Meier survival curves for ccRCC patients stratified by both TMB status (high vs. low) and immunometabolic clusters (C1 vs. C2).

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Comparison, Mutagenesis

    Assessment and confirmation of the predictive performance of the signature in ccRCC. ( A – C ) Scatter plots illustrating the survival status and IMI scores of ccRCC patients in the TCGA training group ( A ), the TCGA testing group ( B ), and the E-MATB-1980 external validation group ( C ). ( D – F ) Kaplan–Meier curves displaying the overall survival situation per IMI scores of the high-IMI group and low-IMI group in the TCGA training group ( D ), the TCGA testing group ( E ), and the E-MATB-1980 external validation group ( F ). ( G – I ) ROC curves demonstrating the predictive performance of IMI with AUC values for 1-year, 3-year, and 5-year OS in ccRCC patients from the TCGA training group ( G ), the TCGA testing group ( H ), and the E-MATB-1980 external validation group ( I ).

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: Assessment and confirmation of the predictive performance of the signature in ccRCC. ( A – C ) Scatter plots illustrating the survival status and IMI scores of ccRCC patients in the TCGA training group ( A ), the TCGA testing group ( B ), and the E-MATB-1980 external validation group ( C ). ( D – F ) Kaplan–Meier curves displaying the overall survival situation per IMI scores of the high-IMI group and low-IMI group in the TCGA training group ( D ), the TCGA testing group ( E ), and the E-MATB-1980 external validation group ( F ). ( G – I ) ROC curves demonstrating the predictive performance of IMI with AUC values for 1-year, 3-year, and 5-year OS in ccRCC patients from the TCGA training group ( G ), the TCGA testing group ( H ), and the E-MATB-1980 external validation group ( I ).

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Biomarker Discovery

    Identification of expression trends of nine IMRGs. ( A ) Differences in signature gene expression between high and low IMI groups in the TCGA database. ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. ( B ) Differences in signature gene expression between normal kidney tissue samples and ccRCC samples in the TCGA database. ( C – K ) The relative expression levels of signature genes between three ccRCC cell lines (786-O, A498, ACHN) and normal renal tubular epithelial cells, HK2. ( L ) The IHC images compared the expression levels of four signature genes between normal renal tissue samples and ccRCC samples in the HPA database ( https://www.proteinatlas.org , accessed on 1 January 2024).

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: Identification of expression trends of nine IMRGs. ( A ) Differences in signature gene expression between high and low IMI groups in the TCGA database. ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. ( B ) Differences in signature gene expression between normal kidney tissue samples and ccRCC samples in the TCGA database. ( C – K ) The relative expression levels of signature genes between three ccRCC cell lines (786-O, A498, ACHN) and normal renal tubular epithelial cells, HK2. ( L ) The IHC images compared the expression levels of four signature genes between normal renal tissue samples and ccRCC samples in the HPA database ( https://www.proteinatlas.org , accessed on 1 January 2024).

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Expressing, Gene Expression

    Verification of UCN promoting proliferation, migration, and invasion of ccRCC. ( A ) Knockdown of the UCN gene in 786-O and ACHN cells, relative mRNA levels in the negative control (NC) group and three siRNA knockdown groups, respectively. **** p < 0.0001 ( B ) The knockdown effect of three siRNAs on the UCN gene at the protein level in two cell lines. The uncropped blots are shown in . ( C ) The proliferation curves of CCK8 in the control group and the knockdown groups of the two cell lines. Any siRNA group has significant statistical differences from the NC group. ( D , E ) Wound-healing assays in control and knockdown groups of the two cell lines. ( F , G ) Transwell invasion assays in control and knockdown groups of the two cell lines.

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: Verification of UCN promoting proliferation, migration, and invasion of ccRCC. ( A ) Knockdown of the UCN gene in 786-O and ACHN cells, relative mRNA levels in the negative control (NC) group and three siRNA knockdown groups, respectively. **** p < 0.0001 ( B ) The knockdown effect of three siRNAs on the UCN gene at the protein level in two cell lines. The uncropped blots are shown in . ( C ) The proliferation curves of CCK8 in the control group and the knockdown groups of the two cell lines. Any siRNA group has significant statistical differences from the NC group. ( D , E ) Wound-healing assays in control and knockdown groups of the two cell lines. ( F , G ) Transwell invasion assays in control and knockdown groups of the two cell lines.

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Migration, Knockdown, Negative Control, Control

    UCN regulates the immune microenvironment and promotes ccRCC progression. ( A ) Schematic illustration of the mouse xenograft tumor model experimental design. ( B – E ) Tumor growth analyses demonstrate reduced tumor volume and weight across different experimental groups, with notable suppression in sh UCN +IgG2a and sh UCN +PD-1 groups. ( F ) Gating strategy for tumor-infiltrating lymphocytes. Representative flow plots showing the identification of Live/CD45+ cells, T cells (CD3+), CD4+ and CD8+ subsets, as well as Tregs and PD-1+ cells. ( G ) Flow cytometry analysis unveils substantial alterations in immune cell subsets in the tumor immune microenvironment. ( H , I ) Representative mIHC staining of tumors (green: CD8, red: Foxp3, blue: DAPI; scale bar, 50 μm.) ( I ) The column diagram showing the counts of spots with CD8+ T cells and Tregs in tumor slides. Data presented as Mean ± SEM. One-way ANOVA was used in ( E , G , I ). * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.

    Journal: Cancers

    Article Title: An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and Identifies UCN -Mediated Immune Evasion as a Therapeutic Vulnerability: Evidence from In Vitro and In Vivo Studies

    doi: 10.3390/cancers18091373

    Figure Lengend Snippet: UCN regulates the immune microenvironment and promotes ccRCC progression. ( A ) Schematic illustration of the mouse xenograft tumor model experimental design. ( B – E ) Tumor growth analyses demonstrate reduced tumor volume and weight across different experimental groups, with notable suppression in sh UCN +IgG2a and sh UCN +PD-1 groups. ( F ) Gating strategy for tumor-infiltrating lymphocytes. Representative flow plots showing the identification of Live/CD45+ cells, T cells (CD3+), CD4+ and CD8+ subsets, as well as Tregs and PD-1+ cells. ( G ) Flow cytometry analysis unveils substantial alterations in immune cell subsets in the tumor immune microenvironment. ( H , I ) Representative mIHC staining of tumors (green: CD8, red: Foxp3, blue: DAPI; scale bar, 50 μm.) ( I ) The column diagram showing the counts of spots with CD8+ T cells and Tregs in tumor slides. Data presented as Mean ± SEM. One-way ANOVA was used in ( E , G , I ). * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001.

    Article Snippet: Human ccRCC cell line 786-O (Accession Number: CVCL_1051) and mouse ccRCC cell line Renca (CVCL_2174) were obtained from American Type Culture Collection (ATCC) (Manassas, Virginia) and cultured in RPMI 1640 medium (Procell, Wuhan, China) containing 10% fetal bovine serum (Procell, China) and Penicillin–Streptomycin (Procell, China).

    Techniques: Flow Cytometry, Staining