TP53 isoform junction reads based analysis in malignant and normal contexts

AbstractTP53 is one of the most frequently altered genes in cancer; it can be inactivated by a number of different mechanisms. NM_000546.6 (ENST00000269305.9) is by far the predominant TP53 isoform, however a few other alternative isoforms have been described to be expressed at much lower levels. To better understand patterns of TP53 alternative isoforms expression in cancer and normal samples we performed exon-exon junction reads based analysis of TP53 isoforms using RNA-seq data from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx) project. TP53 C-terminal alternative isoforms have abolished or severely decreased tumor suppressor activity, and therefore, an increase in fraction of TP53 C-terminal alternative isoforms may be expected in tumors with wild type TP53. Despite our expectation that there would be increase of fraction of TP53 C-terminal alternative isoforms, we observed no substantial increase in fraction of TP53 C-terminal alternative isoforms in TCGA tumors and CCLE cancer cell lines with wild type TP53, likely indicating that TP53 C-terminal alternative isoforms expression cannot be reliably selected for during tumor progression.

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Comparing cancer cell lines and tumor samples by genomic profiles

10.1101/028159 ◽

2015 ◽

Cited By ~ 2

Author(s):

Rileen Sinha ◽

Nikolaus Schultz ◽

Chris Sander

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Line ◽

Cancer Cell Lines ◽

Response To Therapy ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tumor Material

Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method, TumorComparer, to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest (weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.

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Abnormal Expression of NOS3 and Association with Clinical Outcome are Highly Cancer Dependent as Revealed through Pan-Cancer Analysis

10.21203/rs.2.19006/v1 ◽

2019 ◽

Author(s):

Dan Zou ◽

Fei Lv ◽

Yi Yang ◽

Chunjiao Yang ◽

Yang Chen ◽

...

Keyword(s):

Nitric Oxide ◽

Cancer Cell Line ◽

Cancer Therapeutics ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Cancer Genome Atlas ◽

Cancer Tissues ◽

Oxide Synthase ◽

Pan Cancer

Abstract Background: NOS3 (endothelial NOS, eNOS) is a member of the nitric oxide synthase (NOS) enzyme family, mainly participating in nitric oxide (NO) generation. NOS3 has been reported to inhibit apoptosis and promote angiogenesis, proliferation, and invasiveness. However, the expression pattern of NOS3 and its diagnostic and prognostic potential has not been investigated in a pan-cancer perspective. Methods: In this research, data from the Genotype-Tissue Expression (GTEx), the Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutics Response Portal (CTRP) were employed and NOS3 expression was comprehensively analysed in normal tissues, cancer tissues and cell lines. Its relationship with clinical phenotypes and drug responses was also analysed. Results: In normal tissues, NOS3 was expressed at the highest levels in the spleen and the lowest levels in the blood. Compared with the corresponding normal tissues, its expression in tumour was significantly increased in seven tumour types but decreased in eight tumour types. And NOS3 expression was positively or negatively related to tumour stage and overall survival of patients depending on the tumour types. The expression of NOS3 was related to the response to ‘SR8278’. Conclusions: NOS3 was differentially expressed in tumour tissues, and had prognosis value in some tumour types. And its correlation to drug response warrants further investigation.

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P2.01-054 Lung Cancer PD-L1 mRNA Expression Profile and Clinical Outcomes - An Analysis From The Cancer Genome Atlas and Cancer Cell Line Encyclopedia

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2016.11.1106 ◽

2017 ◽

Vol 12 (1) ◽

pp. S818-S819

Author(s):

Jason Roszik ◽

Don Lynn Gibbons ◽

John Heymach ◽

Ara Vaporciyan ◽

Stephen Swisher ◽

...

Keyword(s):

Lung Cancer ◽

Mrna Expression ◽

Cell Line ◽

Clinical Outcomes ◽

Cancer Cell ◽

Cancer Cell Line ◽

The Cancer Genome Atlas ◽

Mrna Expression Profile ◽

Cancer Genome Atlas ◽

Genome Atlas

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Enabling cross-study analysis of RNA-Sequencing data

10.1101/110734 ◽

2017 ◽

Cited By ~ 4

Author(s):

Qingguo Wang ◽

Joshua Armenia ◽

Chao Zhang ◽

Alexander V. Penson ◽

Ed Reznik ◽

...

Keyword(s):

Large Scale ◽

Tissue Expression ◽

Underlying Disease ◽

The Cancer Genome Atlas ◽

Rna Seq ◽

Sequencing Data ◽

Whole Transcriptome Sequencing ◽

Cancer Genome Atlas ◽

Next Generation Sequencing Ngs ◽

Different Sources

AbstractDriven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

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DIANA-miTED: a microRNA tissue expression database

Nucleic Acids Research ◽

10.1093/nar/gkab733 ◽

2021 ◽

Author(s):

Ioannis Kavakiotis ◽

Athanasios Alexiou ◽

Spyros Tastsoglou ◽

Ioannis S Vlachos ◽

Artemis G Hatzigeorgiou

Keyword(s):

Mirna Expression ◽

Cell Types ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Rna Seq ◽

Starting Point ◽

Metadata Quality ◽

Cancer Genome Atlas ◽

Downstream Analysis ◽

Systematic Collection

Abstract microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.

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psichomics: graphical application for alternative splicing quantification and analysis

10.1101/261180 ◽

2018 ◽

Author(s):

Nuno Saraiva-Agostinho ◽

Nuno L. Barbosa-Morais

Keyword(s):

Alternative Splicing ◽

R Package ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Global Studies ◽

Rna Seq ◽

Cellular Processes ◽

Cancer Genome Atlas ◽

Downstream Analysis ◽

User Friendly

Alternative pre-mRNA splicing generates functionally distinct transcripts from the same gene and is involved in the control of multiple cellular processes, with its dysregulation being associated with a variety of pathologies. The advent of next-generation sequencing has enabled global studies of alternative splicing in different physiological and disease contexts. However, current bioinformatics tools for alternative splicing analysis from RNA-seq data are not user-friendly, disregard available exon-exon junction quantification or have limited downstream analysis features. To overcome such limitations, we have developedpsichomics, an R package with an intuitive graphical interface for alternative splicing quantification and downstream dimensionality reduction, differential splicing and gene expression and survival analyses based on The Cancer Genome Atlas, the Genotype-Tissue Expression project and user-provided data. These integrative analyses can also incorporate clinical and molecular sample-associated features. We successfully usedpsichomicsto reveal alternative splicing signatures specific to stage I breast cancer and associated novel putative prognostic factors.

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RUNX1 and REXO2 are associated with the heterogeneity and prognosis of IDH wild type lower grade glioma

Scientific Reports ◽

10.1038/s41598-021-91382-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Haiwei Wang ◽

Xinrui Wang ◽

Liangpu Xu ◽

Ji Zhang ◽

Hua Cao

Keyword(s):

Transcription Factor ◽

Low Frequency ◽

Tumor Grade ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Wild Type ◽

Lower Grade Glioma ◽

Cancer Genome Atlas ◽

Worse Prognosis ◽

Genome Atlas

AbstractBased on isocitrate dehydrogenase (IDH) alterations, lower grade glioma (LGG) is divided into IDH mutant and wild type subgroups. However, the further classification of IDH wild type LGG was unclear. Here, IDH wild type LGG patients in The Cancer Genome Atlas and Chinese Glioma Genome Atlas were divided into two sub-clusters using non-negative matrix factorization. IDH wild type LGG patients in sub-cluster2 had prolonged overall survival and low frequency of CDKN2A alterations and low immune infiltrations. Differentially expressed genes in sub-cluster1 were positively correlated with RUNX1 transcription factor. Moreover, IDH wild type LGG patients with higher stromal score or immune score were positively correlated with RUNX1 transcription factor. RUNX1 and its target gene REXO2 were up-regulated in sub-cluster1 and associated with the worse prognosis of IDH wild type LGG. RUNX1 and REXO2 were associated with the higher immune infiltrations. Furthermore, RUNX1 and REXO2 were correlated with the worse prognosis of LGG or glioma. IDH wild type LGG in sub-cluster2 was hyper-methylated. REXO2 hyper-methylation was associated with the favorable prognosis of LGG or glioma. At last, we showed that, age, tumor grade and REXO2 expression were independent prognostic factors in IDH wild type LGG.

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Carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) in Pancreatic Ductal Adenocarcinoma (PDA): An integrative analysis of a novel therapeutic target

Scientific Reports ◽

10.1038/s41598-019-54545-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Ritu Pandey ◽

Muhan Zhou ◽

Shariful Islam ◽

Baowei Chen ◽

Natalie K Barker ◽

...

Keyword(s):

Cell Adhesion ◽

Pancreatic Ductal Adenocarcinoma ◽

Therapeutic Target ◽

Cell Activity ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Ductal Adenocarcinoma ◽

Wild Type ◽

Cancer Genome Atlas ◽

Novel Therapeutic

AbstractWe investigated biomarker CEACAM6, a highly abundant cell surface adhesion receptor that modulates the extracellular matrix (ECM) in pancreatic ductal adenocarcinoma (PDA). The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) RNA-Seq data from PDA patients were analyzed for CEACAM6 expression and evaluated for overall survival, association, enrichment and correlations. A CRISPR/Cas9 Knockout (KO) of CEACAM6 in PDA cell line for quantitative proteomics, mitochondrial bioenergetics and tumor growth in mice were conducted. We found CEACAM6 is over-expressed in primary and metastatic basal and classical PDA subtypes. Highest levels are in classical activated stroma subtype. CEACAM6 over-expression is universally a poor prognostic marker in KRAS mutant and wild type PDA. High CEACAM6 expression is associated with low cytolytic T-cell activity in both basal and classical PDA subtypes and correlates with low levels of T-REG markers. In HPAF-II cells knockout of CEACAM6 alters ECM-cell adhesion, catabolism, immune environment, transmembrane transport and autophagy. CEACAM6 loss increases mitochondrial basal and maximal respiratory capacity. HPAF-II CEACAM6−/− cells are growth suppressed by >65% vs. wild type in mice bearing tumors. CEACAM6, a key regulator affects several hallmarks of PDA including the fibrotic reaction, immune regulation, energy metabolism and is a novel therapeutic target in PDA.

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PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

International Journal of Molecular Sciences ◽

10.3390/ijms19103250 ◽

2018 ◽

Vol 19 (10) ◽

pp. 3250 ◽

Cited By ~ 9

Author(s):

Anna Sorrentino ◽

Antonio Federico ◽

Monica Rienzo ◽

Patrizia Gazzerro ◽

Maurizio Bifulco ◽

...

Keyword(s):

Family Members ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Gene ◽

Rna Seq ◽

Set Domain ◽

Primary Tumors ◽

Cancer Genome Atlas ◽

Pan Cancer ◽

Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein–protein, protein–RNA, or protein–DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

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