Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TACCO, a Database Connecting Transcriptome Alterations, Pathway Alterations and Clinical Outcomes in Cancers.

ABSTRACT: Because of innumerable cancer sequencing projects, abundant transcriptome expression profiles together with survival data are available from the same patients. Although some expression signatures for prognosis or pathologic staging have been identified from these data, systematically discovering such kind of expression signatures remains a challenge. To address this, we developed TACCO (Transcriptome Alterations in CanCer Omnibus), a database for identifying differentially expressed genes and altered pathways in cancer. TACCO also reveals miRNA cooperative regulations and supports construction of models for prognosis. The resulting signatures have great potential for patient stratification and treatment decision-making in future clinical applications. TACCO is freely available at http://tacco.life.nctu.edu.tw/ .

SUBMITTER: Chou PH

PROVIDER: S-EPMC6405743 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

TACCO, a Database Connecting Transcriptome Alterations, Pathway Alterations and Clinical Outcomes in Cancers.

Chou Po-Hao PH Liao Wei-Chao WC Tsai Kuo-Wang KW Chen Ku-Chung KC Yu Jau-Song JS Chen Ting-Wen TW

Scientific reports 20190307 1

Because of innumerable cancer sequencing projects, abundant transcriptome expression profiles together with survival data are available from the same patients. Although some expression signatures for prognosis or pathologic staging have been identified from these data, systematically discovering such kind of expression signatures remains a challenge. To address this, we developed TACCO (Transcriptome Alterations in CanCer Omnibus), a database for identifying differentially expressed genes and al ...[more]

PMID: 30846808

Similar Datasets

Genetic association and single-cell transcriptome analyses reveal distinct features connecting autoimmunity with cancers.

Project description:Autoimmune diseases (ADs) are at a significantly higher risk of cancers with unclear mechanism. By searching GWAS catalog database and Medline, susceptible genes for five common ADs, including systemic lupus erythematosus (SLE), rheumatoid arthritis, Sjögren syndrome, systemic sclerosis, and idiopathic inflammatory myopathies, were collected and then were overlapped with cancer driver genes. Single-cell transcriptome analysis was performed in the comparation between SLE and related cancer. We identified 45 carcinogenic autoimmune disease risk (CAD) genes, which were mainly enriched in T cell signaling pathway and B cell signaling pathway. Integrated single-cell analysis revealed immune cell signaling was significantly downregulated in renal cancer compared with SLE, while stemness signature was significantly enriched in both renal cancer or lymphoma and SLE in specific subpopulations. Drugs targeting CAD genes were shared between ADs and cancer. Our study highlights the common and specific features between ADs and related cancers, and sheds light on a new discovery of treatments.

| S-EPMC9254016 | biostudies-literature

Systematic Multiomics Analysis of Alterations in C1QBP mRNA Expression and Relevance for Clinical Outcomes in Cancers.

Project description:C1QBP (Complement Component 1 Q Subcomponent-Binding Protein), a multicompartmental protein, participates in various cellular processes, including mRNA splicing, ribosome biogenesis, protein synthesis in mitochondria, apoptosis, transcriptional regulation, and infection processes of viruses. The correlation of C1QBP expression with patient survival and molecular function of C1QBP in relation to cancer progression has not been comprehensively studied. Therefore, we sought to systematically investigate the expression of C1QBP to evaluate the change of C1QBP expression and the relationship with patient survival and affected pathways in breast, lung, colon, and bladder cancers as well as lymphoma. Relative expression levels of C1QBP were analyzed using the Oncomine, Gene Expression Across Normal and Tumor Tissue (GENT), and The Cancer Genome Atlas (TCGA) databases. Mutations and copy number alterations in C1QBP were also analyzed using cBioPortal, and subsequently, the relationship between C1QBP expression and survival probability of cancer patients was explored using the PrognoScan database and the R2: Kaplan Meier Scanner. Additionally, the relative expression of C1QBP in other cancers, and correlation of C1QBP expression with patient survival were investigated. Gene ontology and pathway analysis of commonly differentially coexpressed genes with C1QBP in breast, lung, colon, and bladder cancers as well as lymphoma revealed the C1QBP-correlated pathways in these cancers. This data-driven study demonstrates the correlation of C1QBP expression with patient survival and identifies possible C1QBP-involved pathways, which may serve as targets of a novel therapeutic modality for various human cancers.

| S-EPMC6517981 | biostudies-literature

New insights on PI3K/AKT pathway alterations and clinical outcomes in breast cancer.

Project description:PI3K/AKT signaling pathway plays an important role in tumorigenesis and regulates critical cellular functions including survival, proliferation and metabolism. PIK3CA mutations and AKT activation by phosphorylation (pAKT) are often detected in many cancers and especially at high frequencies in breast cancer. Mounting data suggest that PIK3CA mutations or pAKT are mostly associated with better or insignificant outcomes in estrogen receptor-positive (ER+) early stage breast cancer and tend to be with worse prognosis in ER- disease. pAKT expression has been identified to predict paclitaxel chemotherapy benefit in node-positive breast cancer. Preclinical and neoadjuvant trial data suggest that PIK3CA alterations confer resistance to HER2-targeted therapy and are associated with lower pathological complete response (pCR) rate in HER2-positive breast cancer. However, recent results from randomized clinical trials of adjuvant and metastatic settings show that patients with mutant and wildtype PIK3CA tumors derived similar benefit from anti-HER2 therapy. This article, with our new insights, aims to decipher the mixed data and discusses the influence of the potential confounding factors in the assessments. We also share our views for validation of PI3K/AKT alterations in relation to clinical outcome in the context of specific breast cancer subtypes and treatment modalities towards further advance of the precision medicine for breast cancer treatment.

| S-EPMC7436195 | biostudies-literature

circExp database: an online transcriptome platform for human circRNA expressions in cancers.

Project description:Circular RNA (circRNA) is a highly stable, single-stranded, closed-loop RNA that works as RNA or as a protein decoy to regulate gene expression. In humans, thousands of circRNA transcriptional products precisely express in specific developmental stages, tissues and cell types. Due to their stability and specificity, circRNAs are ideal biomarkers for cancer diagnosis and prognosis. To provide an integrated and standardized circRNA expression profile for human cancers, we performed extensive data curation across 11 technical platforms, collecting 48 expression profile data sets for 18 cancer types and amassing 860 751 expression records. We also identified 189 193 differential expression signatures that are significantly different between normal and cancer samples. All the pre-calculated expression analysis results are organized into 132 plain text files for bulk download. Our online interface, circExp, provides data browsing and search functions. For each data set, a dynamic expression heatmap provides a profile overview. Based on the processed data, we found that 52 circRNAs were consistently and differentially expressed in 20 or more processed analyses. By mapping those circRNAs to their parent protein-coding genes, we found that they may have profoundly affected the survival of 10 797 patients in the The Cancer Genome Atlas pan-cancer data set. In sum, we developed circExp and demonstrated that it is useful to identify circRNAs that have potential diagnostic and prognostic significance for a variety of cancer types. In this online and reusable database, found at http://soft.bioinfo-minzhao.org/circexp, we have provided pre-calculated expression data about circRNAs and their parental genes, as well as data browsing and searching functions. Database URL: http://soft.bioinfominzhao.org/circexp/.

| S-EPMC8299715 | biostudies-literature

The outcomes of pathway database computations depend on pathway ontology.

Project description:Different biological notions of pathways are used in different pathway databases. Those pathway ontologies significantly impact pathway computations. Computational users of pathway databases will obtain different results depending on the pathway ontology used by the databases they employ, and different pathway ontologies are preferable for different end uses. We explore differences in pathway ontologies by comparing the BioCyc and KEGG ontologies. The BioCyc ontology defines a pathway as a conserved, atomic module of the metabolic network of a single organism, i.e. often regulated as a unit, whose boundaries are defined at high-connectivity stable metabolites. KEGG pathways are on average 4.2 times larger than BioCyc pathways, and combine multiple biological processes from different organisms to produce a substrate-centered reaction mosaic. We compared KEGG and BioCyc pathways using genome context methods, which determine the functional relatedness of pairs of genes. For each method we employed, a pair of genes randomly selected from a BioCyc pathway is more likely to be related by that method than is a pair of genes randomly selected from a KEGG pathway, supporting the conclusion that the BioCyc pathway conceptualization is closer to a single conserved biological process than is that of KEGG.

| S-EPMC1540720 | biostudies-literature

Clinical Outcomes of TP53 Mutations in Cancers.

Project description:High-throughput sequencing of cancer genomes is increasingly becoming an essential tool of clinical oncology that facilitates target identification and targeted therapy within the context of precision medicine. The cumulative profiles of somatic mutations in cancer yielded by comprehensive molecular studies also constitute a fingerprint of historical exposures to exogenous and endogenous mutagens, providing insight into cancer evolution and etiology. Mutational signatures that were first established by inspection of the TP53 gene somatic landscape have now been confirmed and expanded by comprehensive sequencing studies. Further, the degree of granularity achieved by deep sequencing allows detection of low-abundance mutations with clinical relevance. In tumors, they represent the emergence of small aggressive clones; in normal tissues, they signal a mutagenic exposure related to cancer risk; and, in blood, they may soon become effective surveillance tools for diagnostic purposes and for monitoring of cancer prognosis and recurrence.

| S-EPMC5008065 | biostudies-literature

ADP-ribosylation Levels Correlate with Gene Expression Patterns and Clinical Outcomes in Ovarian Cancers

Project description:Inhibitors of nuclear poly(ADP-ribose) polymerase (PARP) enzymes (e.g., PARP-1) have improved clinical outcomes in ovarian cancer, especially in patients with BRCA1/2 gene mutations or additional homologous recombination (HR) DNA repair pathway deficiencies. These defects serve as biomarkers for response to PARP inhibitors (PARPi). We sought to identify an additional biomarker that could predict responses to both conventional chemotherapy and PARPi in ovarian cancers. We focused on cellular ADP-ribosylation (ADPRylation), which is catalyzed by PARP enzymes and detected by detection reagents we developed previously. We determined molecular phenotypes of 34 high-grade serous ovarian cancers and associated them with clinical outcomes.

2019-10-01 | GSE136155 | GEO

Selective KRAS G12C inhibitors in non-small cell lung cancer: chemistry, concurrent pathway alterations, and clinical outcomes.

Project description:Cancers harboring mutations in the Kirsten rat sarcoma homolog (KRAS) gene have been associated with poor prognosis and lack of targeted therapies. KRAS mutations occur in approximately one in four patients diagnosed with non-small cell lung cancer (NSCLC) with KRAS G12C mutations harbored at approximately 11-16%. Research into KRAS-driven tumors and analytical chemistry have borne a new class of selective small molecules against the KRAS G12C isoform. Phase II data for sotorasib (AMG510) has demonstrated a 37.1% overall response rate (ORR). Adagrasib (MRTX849) has demonstrated a 45% ORR in an early study. While single agent efficacy has been seen, initial data suggest combination approaches are an opportunity to improve outcomes. Here, we present perspectives on the initial progress in targeting KRAS G12C, examine co-mutations evident in KRAS G12C NSCLC, and comment on potential future combinatorial approaches including SHP2, SOS1, MEK, EGFR, mTOR, CDK, and checkpoint blockade which are currently being evaluated in clinical trials. As of May 28, 2021, sotorasib has achieved US FDA approval for patients with KRAS G12C mutant lung cancer after one line of a prior therapy.

| S-EPMC8630042 | biostudies-literature

Predicting Human Clinical Outcomes using Mouse Multi-Organ Transcriptome

Project description:Approximately 90% of pre-clinically validated drugs fail in clinical trials due to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.

2020-01-09 | GSE142068 | GEO

Diverse somatic mutation patterns and pathway alterations in human cancers

Project description:The systematic characterization of somatic mutations in cancer genomes is essential for understanding the disease and for developing targeted therapeutics. Here we report the identification of 2,576 somatic mutations across approximately 1,800 megabases of DNA representing 1,507 coding genes from 441 tumours comprising breast, lung, ovarian and prostate cancer types and subtypes. Additionally, 373 tumors were assayed for copy number alterations via Agilent 244A CGH arrays and 153 breast, lung, and colon samples were assayed for mRNA abundance with Affymetrix HuEx1 Exon Arrays. This SuperSeries is composed of the SubSeries listed below.

2010-08-24 | GSE23768 | GEO

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