gsea v3.0 software  (Broad Institute Inc)

Journal: iScience

Article Title: Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

doi: 10.1016/j.isci.2025.112023

Figure Lengend Snippet: Identification of hypoxia-related subtypes of glioma (A) Unsupervised clustering results by Consensus Cluster analysis in TCGA cohort (A). (B) t-SNE analysis validated the stratification into two subtypes of glioma in TCGA cohort (B). (C–H) Survival analysis for two subtypes of gliomas in TCGA cohort (C), Chinese Glioma Genome Atlas (CGGA)-693 cohort (D), CGGA-301 cohort (E), CGGA-325 cohort (F), GSE16011 cohort (G), and Rembrandt cohort (H). The log rank test was used to determine the statistical significance of the differences. (I) GSEA analysis of two subtypes of gliomas in the TCGA cohort. Data are represented as mean ± SEM. (J) Correlations between hypoxia-related subtypes and the enrichment scores of several oncogenic pathways in TCGA cohort.

Article Snippet: Exploring the fundamental signaling pathways of the two subtypes was carried out utilizing GSEA software (version 3.0) acquired from the Broad Institute ( http://www.broadinstitute.org/gsea ).

Techniques:

Journal: iScience

Article Title: Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

doi: 10.1016/j.isci.2025.112023

Figure Lengend Snippet: The relationship between hypoxia-associated signature and immune cell infiltrations in glioma (A) Heatmap displaying the correlation between the hypoxia-associated signature and infiltrating cells; (B) The relationship between risk score and the hypoxia subtype; (C) GSEA for the hypoxia signature scores in the TCGA cohort; (D) The expression level of hub genes between glioma with hypoxia signature scores; (E) Representative IHC staining images of PD-L1, CD8, and HIF1A between two risk groups; Scale bar: 100μm. (F) Analysis of IHC scores between two risk groups according to PD-L1, CD8, and HIF1A staining results. Statistic test: two-sided unpaired t test. Data are represented as mean ± SEM. The p values are labeled above each boxplot with asterisks (ns, no significant, ∗∗ p < 0.01, and ∗∗∗ p < 0.001).

Article Snippet: Exploring the fundamental signaling pathways of the two subtypes was carried out utilizing GSEA software (version 3.0) acquired from the Broad Institute ( http://www.broadinstitute.org/gsea ).

Techniques: Expressing, Immunohistochemistry, Staining, Labeling

Journal: iScience

Article Title: Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

doi: 10.1016/j.isci.2025.112023

Figure Lengend Snippet: Identification of hypoxia-related subtypes of glioma (A) Unsupervised clustering results by Consensus Cluster analysis in TCGA cohort (A). (B) t-SNE analysis validated the stratification into two subtypes of glioma in TCGA cohort (B). (C–H) Survival analysis for two subtypes of gliomas in TCGA cohort (C), Chinese Glioma Genome Atlas (CGGA)-693 cohort (D), CGGA-301 cohort (E), CGGA-325 cohort (F), GSE16011 cohort (G), and Rembrandt cohort (H). The log rank test was used to determine the statistical significance of the differences. (I) GSEA analysis of two subtypes of gliomas in the TCGA cohort. Data are represented as mean ± SEM. (J) Correlations between hypoxia-related subtypes and the enrichment scores of several oncogenic pathways in TCGA cohort.

Article Snippet: The gene expression profile of the two subtypes in the TCGA cohort, along with the hallmark gene sets from the MSigDB datasets provided by the Broad Institute, were imported into the GSEA software (version 3.0).

Techniques:

Journal: iScience

Article Title: Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma

doi: 10.1016/j.isci.2025.112023

Figure Lengend Snippet: The relationship between hypoxia-associated signature and immune cell infiltrations in glioma (A) Heatmap displaying the correlation between the hypoxia-associated signature and infiltrating cells; (B) The relationship between risk score and the hypoxia subtype; (C) GSEA for the hypoxia signature scores in the TCGA cohort; (D) The expression level of hub genes between glioma with hypoxia signature scores; (E) Representative IHC staining images of PD-L1, CD8, and HIF1A between two risk groups; Scale bar: 100μm. (F) Analysis of IHC scores between two risk groups according to PD-L1, CD8, and HIF1A staining results. Statistic test: two-sided unpaired t test. Data are represented as mean ± SEM. The p values are labeled above each boxplot with asterisks (ns, no significant, ∗∗ p < 0.01, and ∗∗∗ p < 0.001).

Article Snippet: The gene expression profile of the two subtypes in the TCGA cohort, along with the hallmark gene sets from the MSigDB datasets provided by the Broad Institute, were imported into the GSEA software (version 3.0).

Techniques: Expressing, Immunohistochemistry, Staining, Labeling