ArticleBMC medical genomics2018
The Cancer Omics Atlas: an integrative resource for cancer omics annotations.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Advanced AI-Powered System for Comprehensive Thyroid Cancer Detection and Malignancy Risk Assessment.Life (Basel, Switzerland) · 2025Article
- Article
- MicroRNA Expression Profiling-Potential Molecular Discrimination of Papillary Thyroid Carcinoma Subtypes.Biomedicines · 2024Article
- The genes significantly associated with an improved prognosis and long-term survival of glioblastoma.PloS one · 2023Article
- Differential Expression of Zinc-Dependent HDAC Subtypes and their Involvement in Unique Pathways Associated with Carcinogenesis.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Identifying tumor immunity-associated molecular features in liver hepatocellular carcinoma by multi-omics analysis.Frontiers in molecular biosciences · 2022Article
- Functional succinate dehydrogenase deficiency is a common adverse feature of clear cell renal cancer.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Epithelial-Mesenchymal Transition Gene Signature Related to Prognostic in Colon Adenocarcinoma.Journal of personalized medicine · 2021Article
- Pan-Cancer Analysis Reveals ThatFrontiers in cell and developmental biology · 2021Article
- Article
- Article
- DriverDBv3: a multi-omics database for cancer driver gene research.Nucleic acids research · 2020Article
- Identification of genomic features associated with immunotherapy response in gastrointestinal cancers.World journal of gastrointestinal oncology · 2019Review
- Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes.JCO precision oncology · 2019Article
- Unraveling the role of peroxisome proliferator-activated receptor-β/δ (PPARβ/δ) expression in colon carcinogenesis.NPJ precision oncology · 2019Review
- Identification of Transcriptional Signatures of Colon Tumor Stroma by a Meta-Analysis.Journal of oncology · 2019Article
- Correction to: The Cancer Omics Atlas: an integrative resource for cancer omics annotations.BMC medical genomics · 2018Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Cancer Genome Atlas (TCGA) is an important data resource for cancer biologists and oncologists. However, a lack of bioinformatics expertise often hinders experimental cancer biologists and oncologists from exploring the TCGA resource. Although a number of tools have been developed for facilitating cancer researchers to utilize the TCGA data, these existing tools cannot fully satisfy the large community of experimental cancer biologists and oncologists without bioinformatics expertise.
methodsWe developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn ) for fast and straightforward querying of TCGA "omics" data.
resultsTCOA provides the querying of gene expression, somatic mutations, microRNA (miRNA) expression, protein expression data based on a single molecule or cancer type. TCOA also provides the querying of expression correlation between gene pairs, miRNA pairs, gene and miRNA, and gene and protein. Moreover, TCOA provides the querying of the associations between gene, miRNA, or protein expression and survival prognosis in cancers. In addition, TCOA displays transcriptional profiles across various human cancer types based on the pan-cancer analysis. Finally, TCOA provides the querying of molecular profiles for 2877 immune-related genes in human cancers. These immune-related genes include those that are established or promising targets for cancer immunotherapy such as CTLA4, PD1, PD-L1, PD-L2, IDO1, LAG3, and TIGIT.
conclusionsTCOA is a useful tool that supplies a number of unique and new functions complementary to the existing tools to facilitate exploration of the TCGA resource.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.