scholarly journals PCaDB - a comprehensive and interactive database for transcriptomes from prostate cancer population cohorts

2021 ◽  
Author(s):  
Ruidong Li ◽  
Zhenyu Jia
Keyword(s):  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Public ◽  
Interactive Analysis ◽  
Single Cell Rna Sequencing ◽  
Transcriptomics Data ◽  
Normal Human ◽  
User Friendly

Prostate cancer (PCa) is a heterogeneous disease with highly variable clinical outcomes which presents enormous challenges in the clinical management. A vast amount of transcriptomics data from large PCa cohorts have been generated, providing extraordinary opportunities for the comprehensive molecular characterization of the PCa disease and development of prognostic signatures to accurately predict the risk of PCa recurrence. The lack of an inclusive collection and standard processing of the public transcriptomics datasets constrains the extensive use of the valuable resources. In this study, we present a user-friendly database, PCaDB, for a comprehensive and interactive analysis and visualization of gene expression profiles from 50 public transcriptomics datasets with 7,231 samples. PCaDB also includes a single-cell RNA-sequencing (scRNAseq) dataset for normal human prostates and 30 published PCa prognostic signatures. The advanced analytical methods equipped in PCaDB would greatly facilitate data mining to understand the heterogeneity of PCa and to develop prognostic signatures and machine learning models for PCa prognosis. PCaDB is publicly available at http://bioinfo.jialab-ucr.org/PCaDB/.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2018 ◽  
Author(s):  
R. Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Tree Structures ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

AbstractAdvances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. Consequently, it becomes possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data and further impute temporal gene expression profiles along the reconstructed tree structures. We demonstrate MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2019 ◽  
Vol 47 (17) ◽  
pp. 8961-8974 ◽  
Author(s):  
R Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

Abstract Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

Characterization of gene expression profiles at low and very low doses of ionizing radiation

DNA Repair ◽  
2013 ◽  
Vol 12 (7) ◽  
pp. 508-517 ◽  
Author(s):  
Ingrid Nosel ◽  
Aurélie Vaurijoux ◽  
Joan-Francesc Barquinero ◽  
Gaetan Gruel
Keyword(s):  
Gene Expression ◽  
Ionizing Radiation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Doses

scholarly journals Circulating tumor cell characterization of lung cancer brain metastasis in the cerebrospinal fluid through single-cell transcriptome analysis

2020 ◽  
Author(s):  
Haoyu Ruan ◽  
Yihang Zhou ◽  
Jie Shen ◽  
Yue Zhai ◽  
Ying Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cerebrospinal Fluid ◽  
Single Cell ◽  
Tumor Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Unknown Primary ◽  
Great Promise ◽  
Cell Cycle Gene

AbstractMetastatic lung cancer accounts for about half of the brain metastases (BM). Development of leptomeningeal metastases (LM) are becoming increasingly common, and its prognosis is still poor despite the advances in systemic and local approaches. Cytology analysis in the cerebrospinal fluid (CSF) remains the diagnostic gold standard. Although several previous studies performed in CSF have offered great promise for the diagnostics and therapeutics of LM, a comprehensive characterization of circulating tumor cells (CTCs) in CSF is still lacking. To fill this critical gap of lung adenocarcinoma LM (LUAD-LM), we analyzed the transcriptomes of 1,375 cells from 5 LUAD-LM patient and 3 control samples using single-cell RNA sequencing technology. We defined CSF-CTCs based on abundant expression of epithelial markers and genes with lung origin, as well as the enrichment of metabolic pathway and cell adhesion molecules, which are crucial for the survival and metastases of tumor cells. Elevated expression of CEACAM6 and SCGB3A2 was discovered in CSF-CTCs, which could serve as candidate biomarkers of LUAD-LM. We identified substantial heterogeneity in CSF-CTCs among LUAD-LM patients and within patient among individual cells. Cell-cycle gene expression profiles and the proportion of CTCs displaying mesenchymal and cancer stem cell properties also vary among patients. In addition, CSF-CTC transcriptome profiling identified one LM case as cancer of unknown primary site (CUP). Our results will shed light on the mechanism of LUAD-LM and provide a new direction of diagnostic test of LUAD-LM and CUP cases from CSF samples.

scholarly journals 1100: Gene Expression Profiles of Doxazosin-Treated LNCaP Prostate Cancer Cells

2004 ◽  
Vol 171 (4S) ◽  
pp. 290-290
Author(s):  
José M. Arencibia ◽  
Mónica Del Río ◽  
Ana Bonnin ◽  
Mónica López-Barahona
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostate Cancer Cells

Genomic Fabric Remodeling in Prostate Cancer

2021 ◽  
Author(s):  
Sanda Iacobas ◽  
Dumitru A. Iacobas
Keyword(s):  
Prostate Cancer ◽  
Normal Tissue ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mathematical Object ◽  
P53 Signaling ◽  
Large Populations ◽  
Genomic Characterization ◽  
Meta Analyses

Prostate cancer is a leading cause of death among men but its genomic characterization and best therapeutic strategy are still under debate. The Genomic Fabric Paradigm (GFP) considers the transcriptome as a multi-dimensional mathematical object subjected to a dynamic set of expression correlations among the genes. Here, GFP is applied to gene expression profiles of three (one primary, and two secondary) cancer nodules and the surrounding normal tissue from a surgically removed prostate tumor. GFP was used to determine the regulation and rewiring of the P53 signaling, apoptosis, prostate cancer and several other pathways involved in survival and proliferation of the cancer cells. Genes responsible for the block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy and sustained angiogenesis were found as differently regulated in the three cancer nodules with respect to the normal tissue. The analysis indicates that even histo-pathologically equally graded cancer nodules from the same tumor have substantially different transcriptomic organizations, raising legitimate questions about the validity of meta-analyses comparing large populations of healthy and cancer humans. The study suggests that GFP may provide a personalized alternative to the biomarkers’ approach of cancer genomics.

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