Ontology highlight
ABSTRACT:
PROVIDER: PRJNA989587 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:RNA-seq of 1019 human cancer cell lines from the Cancer Cell Line Encyclopedia
2025-01-23 | E-MTAB-2770 | ExpressionAtlas
Project description:This study aimed to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in patients with esophageal adenocarcinoma (EAC). cfDNA methylation profiling with cfRRBS was performed on 162 blood plasma samples from 33 EAC cancer patients and 28 blood plasma samples from 20 healthy donors. In addition, for reproducibility testing purposes of the method, 9 plasma samples were re-prepped (library was re-made) and re-sequenced once (n=9) or twice (n=1). As a reference for the data deconvolution cfRRBS was performed on 7 EAC tumor tissue (FFPE) samples.
2024-12-04 | E-MTAB-14370 | biostudies-arrayexpress
Project description:Colorectal cancer cell line proteomes
2021-05-25 | E-PROT-18 | ExpressionAtlas
Project description:Toxicogenomics databases are useful for understanding biological responses in individuals because they are derived from well-controlled experiments and include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could add information regarding immune cell trafficking to the database. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats and established a methodology to obtain information regarding immune cells from toxicogenomics databases. The contributions of this work are three-fold. First, we obtained the gene expression profiles of various rat immune cells necessary for deconvolution and constructed a dataset; second, we compared the accuracy of models based on human and mouse datasets and showed the impact of species differences on deconvolution; third, we showed that rat deconvolution could retrieve information regarding immune cell trafficking from toxicogenomics databases. Correspondence: Tadahaya Mizuno
2023-08-15 | GSE239996 | GEO
Project description:Comparing the relative proportions of immune cells in tumor and adjacent normal tissue from NSCLC patients demonstrates the early changes of tumor immunity and provides insights to guide immunotherapy design. We mapped the immune ecosystem using computational deconvolution of bulk transcriptome data from the Cancer Genome Atlas (TCGA) and single cell RNA sequencing (scRNA-seq) data of dissociated tumors from early-stage non-small cell lung cancer (NSCLC) to investigate early immune landscape changes occurring during tumorigenesis. Computational deconvolution of immune infiltrates in 44 NSCLC and matching adjacent normal samples from TCGA showed heterogeneous patterns of alterations in immune cells. The scRNA-seq analyses of 11,485 cells from 4 treatment-naïve NSCLC patients comparing tumor to adjacent normal tissues showed diverse changes of immune cell compositions. Notably, CD8+ T cells and NK cells are present at low levels in adjacent normal tissues, and are further decreased within tumors. Myeloid cells exhibited marked dynamic reprogramming activities, which were delineated with differentiation paths through trajectory analysis. A common differentiation path from CD14+ monocytes to M2 macrophages was identified among the 4 cases, accompanied by up-regulated genes (e.g. ALCAM/CD166, CD59, IL13RA1, IL7R) with enriched functions (adipogenesis, lysosome), and down-regulated genes (e.g. CXCL2, IL1B, IL6R) with enriched functions (TNFa signaling via NF-kB, inflammatory response). Computational deconvolution and single cell sequencing analyses have revealed a highly dynamic immune reprogramming that occurs in early stage NSCLC development, suggesting that normalizing both immune compartments may represent a viable strategy for treatment of early stage cancer and prevention of progression.
2019-04-30 | GSE117570 | GEO
Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.
2024-01-12 | PXD044157 | Pride
Project description:Next generation sequencing of human immune cell subsets across diseases
2025-01-24 | E-GEOD-60424 | ExpressionAtlas
Project description:Breast cancer cell lines - palmitate vs control
2025-01-23 | E-MTAB-2601 | ExpressionAtlas
Project description:We use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further we use gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.
2019-12-10 | GSE135298 | GEO
Project description:Secretome analysis of colorectal cancer cell linesSW620, KM12SM, and KM12L4 cell lines
2023-11-08 | E-PROT-110 | ExpressionAtlas