Journal: NPJ Systems Biology and Applications

Article Title: Association of copy number alterations with the immune transcriptomic landscape in cancer

doi: 10.1038/s41540-026-00649-8

Figure Lengend Snippet: A Overview of the 294,159 bulk transcriptomic profiles collected from the three datasets: GPL570 , ARCHS4, and TCGA. Consensus independent component analysis (c-ICA) was applied to each dataset to disentangle the bulk transcriptomic profiles into statistically independent transcriptional components (TCs). The TCs were then classified as CNA-TCs if they captured the effect of copy number alterations (CNA) based on the transcriptional adaptation to CNA profiling (TACNA). Additionally, TCs that capture immune-related processes using gene set enrichment analysis (GSEA) were defined as immune-TCs. B Heatmaps showing CNA regions captured by the CNA-TCs. Each column corresponds to a CNA-TC, with genes arranged in genomic order. For each CNA-TC, regions where many genes have high gene weights—indicating a CNA effect as determined by TACNA—are marked in red (see inset example). Only the red-marked regions, which represent the specific CNA effect captured by the corresponding CNA-TC, are shown. The CNA-TCs are sorted based on the position of the CNA region they capture. C Heatmap showing the z-value of GSEA for each immune-TC across all immune-related gene sets from Gene Ontology—Biological Process and REACTOME.

Article Snippet: Spatial transcriptomic profiles were obtained from the 10x Genomics website ( https://www.10xgenomics.com ).

Techniques: Capture-C

Journal: NPJ Systems Biology and Applications

Article Title: Association of copy number alterations with the immune transcriptomic landscape in cancer

doi: 10.1038/s41540-026-00649-8

Figure Lengend Snippet: A Three examples of immune-TC activity across cell types are shown. Single-cell RNA sequencing included 114,253 cells from 181 patients with 13 different cancer types from the single-cell tumor immune atlas for precision oncology. The transcriptomic profile of each cell was projected onto the GPL570 immune-TCs. Cell annotation was based on the labels defined in the immune atlas. Box plot colors represent major cell type groups. The boxplot displays the median as the central line, with box hinges representing the second and third quartiles, whiskers extending by half the interquartile range, and outliers shown as individual dots. B Three examples of tumor spatial transcriptomic datasets from 10x Genomics Visium are shown. The transcriptomic profile of each spatial spot was projected onto the GPL570 CNA- and immune-TCs, and CNA burden was inferred. The spatial organization of the activity of three immune-TCs is shown for each tumor.

Article Snippet: Spatial transcriptomic profiles were obtained from the 10x Genomics website ( https://www.10xgenomics.com ).

Techniques: Activity Assay, Single Cell, RNA Sequencing

Journal: Journal of Translational Medicine

Article Title: Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms

doi: 10.1186/s12967-025-06733-7

Figure Lengend Snippet: The expression pattern and tissue localization of PPARG in tumor samples. ( A ) PPARG expression levels in tumor and normal samples of the TCGA dataset. ( B ) PPARG was correlated with pathological grades in the TCGA dataset. ( C ) Survival analysis of OS time between high and low-PPARG groups. ( D ) Survival analysis of DSS time between high and low-PPARG groups. ( E ) Correlation analyses between PPARG expression and tumor phenotypes. ( F , H , J ) PPARG expression in different cell types of spatial transcriptomics. F : LIHC1, H : LIHC2, J : LIHC3. ( G , I , K ) The comparisons of PPARG expression levels between malignant and normal samples. ( L ) The visualizations of the relationship between PPARG expression and various components of TME

Article Snippet: Spatial transcriptomics (ST) profiles were obtained from Mendeley Data (skrx2fz79n) [ ].

Techniques: Expressing