Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The Progenetix oncogenomic resource in 2021.

ABSTRACT: In cancer, copy number aberrations (CNAs) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138 663 (including 115 357 tumor) copy number variation (CNV) profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive National Cancer Institute Thesaurus cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org.

SUBMITTER: Huang Q

PROVIDER: S-EPMC8285936 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Exploring the OncoGenomic Landscape of cancer.

Project description:BackgroundThe widespread incorporation of next-generation sequencing into clinical oncology has yielded an unprecedented amount of molecular data from thousands of patients. A main current challenge is to find out reliable ways to extrapolate results from one group of patients to another and to bring rationale to individual cases in the light of what is known from the cohorts.ResultsWe present OncoGenomic Landscapes, a framework to analyze and display thousands of cancer genomic profiles in a 2D space. Our tool allows users to rapidly assess the heterogeneity of large cohorts, enabling the comparison to other groups of patients, and using driver genes as landmarks to aid in the interpretation of the landscapes. In our web-server, we also offer the possibility of mapping new samples and cohorts onto 22 predefined landscapes related to cancer cell line panels, organoids, patient-derived xenografts, and clinical tumor samples.ConclusionsContextualizing individual subjects in a more general landscape of human cancer is a valuable aid for basic researchers and clinical oncologists trying to identify treatment opportunities, maybe yet unapproved, for patients that ran out of standard therapeutic options. The web-server can be accessed at https://oglandscapes.irbbarcelona.org /.

| S-EPMC6090738 | biostudies-literature

BRENDA, the ELIXIR core data resource in 2021: new developments and updates.

Project description:The BRENDA enzyme database (https://www.brenda-enzymes.org), established in 1987, has evolved into the main collection of functional enzyme and metabolism data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted from ∼157 000 primary literature references, combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, ligand information, taxonomic, bibliographic, and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.

| S-EPMC7779020 | biostudies-literature

3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome.

Project description:Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. '3DIV' is freely available at http://3div.kr.

| S-EPMC7778885 | biostudies-literature

Ahmed_Shireman Brain 2021 ChIPSeq

Project description:Glioblastoma is a primary brain cancer with a near 100% recurrence rate. Upon recurrence, the tumor is resistant to all conventional therapies, and because of this, 5-year survival is dismal. One of the major drivers of this high recurrence rate is the ability of GBM cells to adapt to complex changes within the tumor microenvironment. To elucidate the molecular mechanisms of this adaptation, specifically during chemotherapy, we employed ChIP-Sequencing and gene expression analysis. We identified a molecular circuit in which the expression of ciliary protein ALR13B is epigenetically regulated to promote adaptation to chemotherapy. Immuno-precipitation combined with Liquid Chromatography-Mass Spectrometry binding partner analysis revealed that that ARL13B interacts with the purine biosynthetic enzyme IMPDH2. Further, radioisotope tracing revealed that this interaction function as a negative regulator for purine salvaging. Inhibition of ARL13B-IMPDH2 interaction enhances temozolomide (TMZ)-induced DNA damage by forcing GBM cells to rely on the purine salvage pathway. Targeting the ARLI3B-IMPDH2 circuit can be achieved by using an FDA-approved drug, Mycophenolate Mofetil, that can block the IMPDH2 activity and enhance the therapeutic efficacy of TMZ. Our results suggest and support clinical evaluation of MMF in combination with TMZ treatment in glioma patients.

2021-01-07 | GSE164352 | GEO

Oncogenomic analysis identifies novel biomarkers for tumor stage mycosis fungoides.

Project description:Patients with mycosis fungoides (MF) developing tumors or extracutaneous lesions usually have a poor prognosis with no cure has so far been available. To identify potential novel biomarkers for MF at the tumor stage, a genomic mapping of 41 cutaneous lymphoma biopsies was used to explore for significant genes.The gene expression profiling datasets of MF were obtained from Gene Expression Omnibus database (GEO). Gene modules were simulated using Weighted Gene Co-expression Network Analysis (WGCNA) and the top soft-connected genes (hub genes) were filtrated with a threshold (0.5). Subsequently, module eigengenes were calculated and significant biological pathways were enriched based on the KEGG database.Four genetic modules were simulated with 3263 genes collected from the whole genomic profile based on cutoff values. Significant diseases genetic terminologies associated with tumor stage MF were found in black module. Subsequently, 13 hub genes including CFLAR, GCNT2, IFNG, IL17A, IL22, MIP, PLCG1, PTH, PTPN6, REG1A, SNAP25, SUPT7L, and TP63 were shown to be related to cutaneous T-cell lymphoma (CTCL) and adult T-cell lymphoma/leukemia (ATLL).In summary, in addition to the reported genes (IL17F, PLCG1, IFNG, and PTH) in CTCL/ATLL, the other high instable genes may serve as novel biomarkers for the regulation of the biological processes and molecular mechanisms of CTLT (MF/SS).

| S-EPMC6392713 | biostudies-literature

Transcriptional oncogenomic hot spots in Barrett's adenocarcinomas: serial analysis of gene expression.

Project description:Serial analysis of gene expression (SAGE) provides quantitative and comprehensive expression profiling in a given cell population. In our efforts to define gene expression alterations in Barrett's-related adenocarcinomas (BA), we produced eight SAGE libraries and obtained a total of 457,894 expressed tags with 32,035 (6.9%) accounting for singleton tags. The tumor samples produced an average of 71,804 tags per library, whereas normal samples produced an average of 42,669 tags per library. Our libraries contained 67,200 unique tags representing 16,040 known gene symbols. Five hundred and sixty-eight unique tags were differentially expressed between BAs and normal tissue samples (at least twofold; P<or=0.05), 395 of these matched to known genes. Interestingly, the distribution of altered genes was not uniform across the human genome. Overexpressed genes tended to cluster in well-defined hot spots located in certain chromosomes. For example, chromosome 19 had 26 overexpressed genes, of which 18 mapped to 19q13. Using the gene ontology approach for functional classification of genes, we identified several groups that are relevant to carcinogenesis. We validated the SAGE results of five representative genes (ANPEP, ECGF1, PP1201, EIF5A1, and GKN1) using quantitative real-time reverse-transcription PCR on 31 BA samples and 26 normal samples. In addition, we performed an immunohistochemistry analysis for ANPEP, which demonstrated overexpression of ANPEP in 6/7 (86%) Barrett's dysplasias and 35/65 (54%) BAs. ANPEP is a secreted protein that may have diagnostic and/or prognostic significance for Barrett's progression. The use of genomic approaches in this study provided useful information about the molecular pathobiology of BAs.

| S-EPMC7165894 | biostudies-literature

Prioritization of candidate cancer genes--an aid to oncogenomic studies.

Project description:The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

| S-EPMC2566894 | biostudies-other

Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach.

Project description:The heterogeneity and instability of human tumors hamper straightforward identification of cancer-causing mutations through genomic approaches alone. Herein we describe a mouse model of liver cancer initiated from progenitor cells harboring defined cancer-predisposing lesions. Genome-wide analyses of tumors in this mouse model and in human hepatocellular carcinomas revealed a recurrent amplification at mouse chromosome 9qA1, the syntenic region of human chromosome 11q22. Gene-expression analyses delineated cIAP1, a known inhibitor of apoptosis, and Yap, a transcription factor, as candidate oncogenes in the amplicon. In the genetic context of their amplification, both cIAP1 and Yap accelerated tumorigenesis and were required to sustain rapid growth of amplicon-containing tumors. Furthermore, cIAP1 and Yap cooperated to promote tumorigenesis. Our results establish a tractable model of liver cancer, identify two oncogenes that cooperate by virtue of their coamplification in the same genomic locus, and suggest an efficient strategy for the annotation of human cancer genes.

| S-EPMC3026384 | biostudies-literature

Oncogenomic analysis of mycosis fungoides reveals major differences with SÈzary syndrome

Project description:Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma (CTCL), is a malignancy of mature, skin-homing T cells. Sézary syndrome (Sz) is often considered to represent a leukemic phase of MF. In this study the pattern of numerical chromosomal alterations in MF tumor samples was defined using array-based CGH; simultaneously gene expression was analyzed using microarrays. Highly recurrent chromosomal alterations in MF include copy number gain of 7q36, 7q21-7q22 and loss of 5q13 and 9p21. This pattern characteristic of MF differs markedly from chromosomal alterations observed in Sz. Integration of data from array-based CGH and gene expression analysis yielded several candidate genes with potential relevance in the pathogenesis of MF. We confirmed that the FASTK and SKAP1 genes, residing in loci with recurrent gain, demonstrated increased expression. The RB1 and DLEU1 tumor suppressor genes showed diminished expression associated with loss. In addition, it was found that presence of chromosomal alterations on 9p21, 8q24 and 1q21-1q22 was associated with poor prognosis in patients with MF. This study provides novel insight into genetic alterations underlying MF. Furthermore, our analysis uncovered genomic differences between MF and Sz, which suggest that the molecular pathogenesis and therefore therapeutic requirements of these CTCLs may be distinct. To identify candidate oncogenes and tumor suppressor genes residing in chromosomal regions with recurrent copy number alteration in MF. To this end chromosomal alteration and gene expression patterns of 22 MF tumor samples were integrated to determine which genes located in minimal common regions (MCRs) with CNA demonstrated dysregulated expression associated with chromosomal alteration Keywords: aCGH and gene expression integration 22 tumors for aCGH and gene expression

| S-ECPF-GEOD-12902 | biostudies-other

Identification of a therapeutic strategy targeting amplified FGF19 in liver cancer by Oncogenomic screening.

Project description:We screened 124 genes that are amplified in human hepatocellular carcinoma (HCC) using a mouse hepatoblast model and identified 18 tumor-promoting genes, including CCND1 and its neighbor on 11q13.3, FGF19. Although it is widely assumed that CCND1 is the main driving oncogene of this common amplicon (15% frequency in HCC), both forward-transformation assays and RNAi-mediated inhibition in human HCC cells established that FGF19 is an equally important driver gene in HCC. Furthermore, clonal growth and tumorigenicity of HCC cells harboring the 11q13.3 amplicon were selectively inhibited by RNAi-mediated knockdown of CCND1 or FGF19, as well as by an anti-FGF19 antibody. These results show that 11q13.3 amplification could be an effective biomarker for patients most likely to respond to anti-FGF19 therapy.

| S-EPMC3061399 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data