Journal: Oncoimmunology

Article Title: Identification of a unique tumor cell subset employing myeloid transcriptional circuits to create an immunomodulatory microenvironment in glioblastoma

doi: 10.1080/2162402X.2022.2030020

Figure Lengend Snippet: Single-cell RNA sequencing identifies a novel tumor cell subset for orchestrating immunosuppression. (a) t-SNE plot showing 16 major cell types using the single-cell RNA sequencing data from 5 cases of GBMs. TAM, Tumor-associated macrophages; RBC, Red blood cell; OPC, Oligodendrocyte progenitor cell. (b) Dot plots showing the distinct cell state markers in the indicated tumor cell subsets (TC-1, -2, -3, -4, -5, and -6). Dot size was proportional to the fraction of cells expressing specific genes. Color intensity corresponds to the relative expression of specific genes. TC, Tumor cell; NPC, Neural progenitor cell; AC, Astrocyte; MES, Mesenchymal. (c) Enrichment analyses of the differential expressed genes in TC-6 versus other subsets (log2 fold-change > 0.5, min.pct > 0.10). The bubble size indicates the number of genes in each term, and different colors correspond to different adjusted p -values. (d) Heatmap showing the total number of interactions between cell types inferred from whole clusters of GBMs using CellphoneDB. Oligo., Oligodendrocyte. (e) Overview of representative ligand–receptor interactions between tumor cells and immune cells. P -values are indicated by circle size, the average expression of interacting molecules in cluster 1 and 2 are indicated by colors. (f) Correlation plot between TC-6 subset and TAM subset in TCGA GBM cohort, correlation coefficient was listed on the top. (g) Dot plots showing the expression of known immune checkpoints ( PD-L1, IDO1, NT5E , and LGALS9 ) across the TC subsets.

Article Snippet: The Cancer Genome Atlas (TCGA) GBM gene expression data (Affymetrix Human Genome U133A microarray platform, n = 529) and the corresponding patient clinical information data were from the UCSC data portal ( https://xenabrowser.net ).

Techniques: RNA Sequencing Assay, Expressing

Journal: Oncoimmunology

Article Title: Identification of a unique tumor cell subset employing myeloid transcriptional circuits to create an immunomodulatory microenvironment in glioblastoma

doi: 10.1080/2162402X.2022.2030020

Figure Lengend Snippet: Novel GBM subtyping based on TC-6 core regulons. (a) The workflow of TC-6 regulons-based subtyping of human GBMs. (b) Consensus clustering based on metagenes from core regulons identified three subgroups. (c) t-SNE analysis of the three subtypes. (d-e) Kaplan–Meier survival analyses of overall survival in GBMs with C3 and non-C3 subtypes using the TCGA mRNA cohort (d) and CGGA RNA-seq cohort (e). **, p < 0.01; *, p < 0.05.

Article Snippet: The Cancer Genome Atlas (TCGA) GBM gene expression data (Affymetrix Human Genome U133A microarray platform, n = 529) and the corresponding patient clinical information data were from the UCSC data portal ( https://xenabrowser.net ).

Techniques: RNA Sequencing Assay

Journal: Oncoimmunology

Article Title: Identification of a unique tumor cell subset employing myeloid transcriptional circuits to create an immunomodulatory microenvironment in glioblastoma

doi: 10.1080/2162402X.2022.2030020

Figure Lengend Snippet: IFN-related DNA damage resistance signature informs therapeutic resistance to chemo/radio-therapy treatment in C3-subtype GBMs. (a) The landscape of clinical and molecular characteristics among different subtypes (C1, C2, C3). The normalized ssGSEA score for functional gene sets was plotted through a heatmap. G-CIMP, Glioma-CpG island methylator phenotype; MGMT, O6-methylguanine-DNA methyltransferase; ECM, Extracellular matrix. (b) GSEA plot of the enrichment of IFN-related DNA damage resistance signature in the C3 subtype relative to the non-C3 subtypes (C1 and C2). Leading-edge genes were labeled as the red dotted line (left panel). Heatmap in the right panel showed the expression of these leading-edge genes in five pairs of primary-recurrent GBM samples from the TCGA database. (c) GSEA plot of C3 over-expressed geneset (log2 fold-change >1) in temozolomide (TMZ)/radiation-resistant group relative to the TMZ/radiation-sensitive group. (d) Rankings of the library of integrated network-based cellular signatures (LINCS) compounds based on their concordance with TMZ/radiation therapy and discordance with C3 signature. The x-axis value suggests the concordant degree with TMZ/radiation and the y-axis value suggests the discordance with C3 signature.

Article Snippet: The Cancer Genome Atlas (TCGA) GBM gene expression data (Affymetrix Human Genome U133A microarray platform, n = 529) and the corresponding patient clinical information data were from the UCSC data portal ( https://xenabrowser.net ).

Techniques: Functional Assay, Labeling, Expressing

Journal: Oncoimmunology

Article Title: Identification of a unique tumor cell subset employing myeloid transcriptional circuits to create an immunomodulatory microenvironment in glioblastoma

doi: 10.1080/2162402X.2022.2030020

Figure Lengend Snippet: Development of an 11-gene C3 classifier for GBM molecular diagnosis. (a) Plots of sensitivity (red) and specificity (black) of the shrinkage parameter (thresholds) computed by cross-validation. The value (7.9, indicaded in blue line) yielded a subset of 11 genes with the most optimized efficiency. (b) Percentage of classified samples using the 11-gene classifier for C3 subtype in the indicated GBM datasets. The TCGA mRNA (training set) was employed as the predictor model. TCGA mRNA dataset, TCGA RNA-seq dataset, CGGA RNA-seq dataset were used as the testing sets. (c) Unsupervised hierarchical clustering of the TCGA mRNA testing set based on the 11-gene classifier. Metagenes-based subtypes are indicated with different colors at the top column. (d-e) Kaplan–Meier analyses of GBM patients from the TCGA dataset (d) and CGGA dataset (e) based on the 11-gene classifier of C3 subtype. (f) Unsupervised hierarchical clustering of six human GBM samples of RNA-seq data based on the 11-gene classifier of C3 subtype. (g) Enrichment score of TC-6 signature and IFN-related DNA damage resistance in human GBMs with non-C3 and C3 subtypes. The data was presented as the mean ± SEM. **, p < 0.01; *, p < 0.05.

Article Snippet: The Cancer Genome Atlas (TCGA) GBM gene expression data (Affymetrix Human Genome U133A microarray platform, n = 529) and the corresponding patient clinical information data were from the UCSC data portal ( https://xenabrowser.net ).

Techniques: RNA Sequencing Assay

Journal:

Article Title: Meta-Analysis of Microarray Results: Challenges, Opportunities, and Recommendations for Standardization

doi: 10.1016/j.gene.2007.06.016

Figure Lengend Snippet: Sources of variation in microarray analysis.

Article Snippet: The impact of data analysis strategies was carefully considered in the MAQC analysis, and it suggests that identifying DEGs with fold-changes or ratios, in combination with p-values, increases the concordance between different platforms and laboratories ( Shi et al., 2006 ). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 caption a8 The effect of analytical strategies on the number of differentially expressed genes LEFT: A set of microarray data (Affymetrix human U133A array, 22,283 genes total) from drug-treated vascular cells was analyzed by t-test at different p-value thresholds (α=.001, 0.005, 0.05, or 0.10, n=9 pairs) and the number of differentially expressed genes (DEGs) recorded (no correction for multiple testing).

Techniques: Microarray

Journal:

Article Title: Meta-Analysis of Microarray Results: Challenges, Opportunities, and Recommendations for Standardization

doi: 10.1016/j.gene.2007.06.016

Figure Lengend Snippet: LEFT: A set of microarray data (Affymetrix human U133A array, 22,283 genes total) from drug-treated vascular cells was analyzed by t-test at different p-value thresholds (α=.001, 0.005, 0.05, or 0.10, n=9 pairs) and the number of differentially expressed genes (DEGs) recorded (no correction for multiple testing). RIGHT: The same data was analyzed for DEGs by using a t-test (α=0.01) or a 2-fold change with different numbers of replicate pairs (1-9).

Article Snippet: The impact of data analysis strategies was carefully considered in the MAQC analysis, and it suggests that identifying DEGs with fold-changes or ratios, in combination with p-values, increases the concordance between different platforms and laboratories ( Shi et al., 2006 ). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 caption a8 The effect of analytical strategies on the number of differentially expressed genes LEFT: A set of microarray data (Affymetrix human U133A array, 22,283 genes total) from drug-treated vascular cells was analyzed by t-test at different p-value thresholds (α=.001, 0.005, 0.05, or 0.10, n=9 pairs) and the number of differentially expressed genes (DEGs) recorded (no correction for multiple testing).

Techniques: Microarray