ArticleCancer informatics2011
An integrative genomics approach to biomarker discovery in breast cancer.
Article in Cancer informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Delineation of the Germline and Somatic Mutation Interaction Landscape in Triple-Negative and Non-Triple-Negative Breast Cancer.International journal of genomics · 2020Article
- A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder.International journal of molecular sciences · 2017Article
- pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data.Bioinformatics (Oxford, England) · 2017Article
- Integrative genomic analysis for the discovery of biomarkers in prostate cancer.Biomarker insights · 2014Article
- Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome.BMC cancer · 2013Review
- Novel Integrative Genomics Approach for Associating GWAS Information with Intrinsic Subtypes of Breast Cancer.Cancer informatics · 2013Article
- An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer.Cancer informatics · 2013Article
- Comprehensive assessment and network analysis of the emerging genetic susceptibility landscape of prostate cancer.Cancer informatics · 2013Article
- Neurobiology meets genomic science: the promise of human-induced pluripotent stem cells.Development and psychopathology · 2012Article
- Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.Briefings in bioinformatics · 2012Article
- Integrative Analysis of Response to Tamoxifen Treatment in ER-Positive Breast Cancer Using GWAS Information and Transcription Profiling.Breast cancer : basic and clinical research · 2012Article
- Antiestrogen Resistance and the Application of Systems Biology.Drug discovery today. Disease mechanisms · 2012Article
- Role of Genetic Variations inCancer genomics & proteomicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the combined contribution of multiple genetic variants acting within genes and putative biological pathways, and to identify novel genes and biological pathways that could not be identified using traditional GWAS. The results show that genes containing SNPs associated with risk for breast cancer are functionally related and interact with each other in biological pathways relevant to breast cancer. Additionally, we identified novel genes that are co-expressed and interact with genes containing SNPs associated with breast cancer. Integrative analysis combining GWAS information with gene expression data provides functional bridges between GWAS findings and biological pathways involved in breast cancer.
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.