msigdb hallmark gene set collection  (Broad Institute Inc)

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: High clonal dispersal correlates with increased activity of key processes and signaling pathways in vitro (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vitro . Created with BioRender.com . (B) Representative images of the indicated cell lines ( n = 3), transduced with LeGO-NLS constructs. Scale bar: 100 μm, applies to all images. (C) Dispersal scores of indicated cell lines, mean + SD. (D) Pearson’s correlation between the dispersal score and growth rate. (E) Correlation between the dispersal score in CRC cell lines ( n = 13) and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Activity Assay, Protein-Protein interactions, In Vitro, Transduction, Construct, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Cell-specific dispersal scores are maintained within in vivo xenograft models (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vivo . Created with BioRender.com . (B) Representative images of xenografts formed by the indicated LeGO-NLS-transduced cell lines ( n = 3). Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: A robust dispersal gene signature based on experimentally observed clonal dispersal (A and B) Correlation between gene expression and clonal dispersal of CRC cell lines in vitro (A) and in vivo (B). Significantly positively correlated genes are indicated by the dashed boxes. (C) Venn diagram showing numbers of genes that are positively and significantly correlated with the dispersal scores in either in vitro or in vivo models. Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines. Each dot represents a different CRC cell line, and red dots represent the cell lines used in this study. (F) Dispersal gene signature in CMS1 ( n = 12), CMS2 ( n = 79), CMS3 ( n = 8), and CMS4 ( n = 36) CRC cell lines. Each dot represents a different CRC cell line. Significance was assessed using unpaired Student’s t tests. ∗ p < 0.05.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Gene Expression, In Vitro, In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Dispersal signature correlates with negative clinical outcomes (A) Dispersal gene signature ( Z score) in colon tissue of healthy controls ( n = 72), normal colon tissue of CRC patients ( n = 77), or CRC tissue ( n = 132) ( GSE199057 dataset). (B) Dispersal gene signature ( Z score) in CRC adenoma ( n = 132) and cancer patients ( n = 573) in a microarray meta-dataset. , , , , , , , , , , , (C) Fold change of dispersal signature gene expression between normal and tumor tissue, in different cancer types. (D) Correlation between dispersal gene signature expression and human MSigDB hallmark gene sets in CRC samples ( n = 673, TCGA COAD-READ dataset). (E) Correlation between dispersal gene signature and EMT signature in CRC samples ( n = 673, TCGA COAD-READ dataset). Each dot represents a different tumor sample. (F) Dispersal gene signature expression ( Z score) in CMS1 ( n = 68), CMS2 ( n = 207), CMS3 ( n = 64), and CMS4 ( n = 118) CRCs (TCGA COAD-READ dataset). Each dot represents a different tumor sample. (G) Correlation between copy-number heterogeneity (CNH) and dispersal gene signature ( Z score) in microsatellite stable (MSS) CRC tumors (TCGA COAD-READ dataset). (H) Dispersal gene signature ( Z score) of non-relapsing ( n = 222) and relapsed CRC tumors ( n = 77) ( GSE14333 dataset). (I and J) (I) Overall survival probability and (J) recurrence-free survival probability of CRC patients with either high or low expression of the dispersal gene signature (TCGA COAD-READ dataset). (K) Overall survival probability of CMS4 patients with either high or low dispersal gene signature (TCGA COAD-READ dataset). Significance was assessed using unpaired Student’s t tests for comparisons between two groups, ANOVA followed by a post hoc test for multiple group comparisons and the chi-squared test for survival analysis. Ns, not significant, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Microarray, Gene Expression, Expressing

Journal: Journal of hepatology

Article Title: Molecular Classification and Therapeutic Targets in Extrahepatic Cholangiocarcinoma

doi: 10.1016/j.jhep.2020.03.008

Figure Lengend Snippet: (A) Solutions for transcriptome-based unsupervised classification of eCCA using non-negative matrix factorization consensus are shown for k = 2 to k = 5 classes; being four the number of classes with the highest cophenetic coefficient. Heatmaps of: (B) hallmark gene sets from MSigDB collections and (C) immune subpopulations inferred by gene expression of immune metagenes described in The Cancer Immunome Atlas significantly enriched in any of the four molecular classes of eCCA. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to obtain the enrichment score, representing the degree of which the genes in a particular gene set are coordinately up- or down-regulated. Samples from the same molecular class were represented with a normalized enrichment score. P values between a specific molecular class and the rest were calculated using T-Test. Box plots representing the estimation of: (D) stromal; and (E) immune compartment in each eCCA molecular class according to virtual microdissection of tumor-microenvironment using gene expression data (ESTIMATE package). P values were calculated using a two-sided T-test. (F) Relative RNA expression of cell of origin markers (stem cell, hepatocyte and biliary) in the four molecular eCCA classes in comparison to normal bile duct. P values were calculated using a two-sided T-test. Error bars represent 95% confidence intervals.

Article Snippet: Heatmaps of: (B) hallmark gene sets from MSigDB collections and (C) immune subpopulations inferred by gene expression of immune metagenes described in The Cancer Immunome Atlas significantly enriched in any of the four molecular classes of eCCA.

Techniques: Gene Expression, Laser Capture Microdissection, RNA Expression, Comparison