ArticleScientific reports2019
MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer.Cancer biology & therapy · 2021Pooled it
- High-dimensional analysis of the adenosine pathway in high-grade serous ovarian cancer.Journal for immunotherapy of cancer · 2021Pooled it
- Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer.Breast cancer research : BCR · 2026Article
- Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.iScience · 2026Article
- A machine learning assay to predict disease recurrence in hormone receptor-positive breast cancer.ESMO open · 2026Article
- Using semantic search to find publicly available gene-expression datasets.Bioinformatics (Oxford, England) · 2026Article
- Purinergic Signaling in Ovarian Carcinoma.Advanced pharmaceutical bulletin · 2025Review
- MBNL2 enhances cisplatin resistance by regulating apoptosis in ovarian cancer cells.BMB reports · 2025Article
- Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer.Cancer cell international · 2025Article
- Machine learning unveils key Redox signatures for enhanced breast Cancer therapy.Cancer cell international · 2024Article
- Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration.ACS pharmacology & translational science · 2024Review
- Clinically relevant gene signatures provide independent prognostic information in older breast cancer patients.Breast cancer research : BCR · 2024Article
- Integrating PANoptosis insights to enhance breast cancer prognosis and therapeutic decision-making.Frontiers in immunology · 2024Article
- Deciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction.Frontiers in immunology · 2024Article
- Large-scale phenogenomic analysis of human cancers uncovers frequent alterations affecting SMC5/6 complex components in breast cancer.NAR cancer · 2023Article
- A20 affects macrophage polarization through the NLRP3 inflammasome signaling pathway and promotes breast cancer progression.Experimental and therapeutic medicine · 2023Article
- Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer.PLoS computational biology · 2023Article
- CD73 Inhibits cGAS-STING and Cooperates with CD39 to Promote Pancreatic Cancer.Cancer immunology research · 2023Article
- A pairwise strategy for imputing predictive features when combining multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- STAT3 induces breast cancer growth via ANGPTL4, MMP13 and STC1 secretion by cancer associated fibroblasts.Oncogene · 2022Article
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8 authors.
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Abstract
A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.
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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.