ArticleBMC systems biology2010
An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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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
39 citing papers in PubMed, 75 citations in OpenAlex.
- Integrating multi-omics data: Methods and applications in human complex diseases.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.BMC medical informatics and decision making · 2024Article
- Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response.British journal of cancer · 2024Article
- Article
- A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection.Proteomes · 2022Review
- Whole genome copy number analyses reveal a highly aberrant genome in TP53 mutant lung adenocarcinoma tumors.BMC cancer · 2021Article
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- Prospects and challenges of multi-omics data integration in toxicology.Archives of toxicology · 2020Review
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Data integration by multi-tuning parameter elastic net regression.BMC bioinformatics · 2018Article
- Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder.IEEE computational intelligence magazine · 2018Article
- From systems biology to P4 medicine: applications in respiratory medicine.European respiratory review : an official journal of the European Respiratory Society · 2018Review
- Integrative analysis of gene expression and methylation data for breast cancer cell lines.BioData mining · 2018Article
- Article
- More Is Better: Recent Progress in Multi-Omics Data Integration Methods.Frontiers in genetics · 2017Review
- SIFORM: shared informative factor models for integration of multi-platform bioinformatic data.Bioinformatics (Oxford, England) · 2016Article
- Methods for the integration of multi-omics data: mathematical aspects.BMC bioinformatics · 2016Article
- Developmental genes significantly afflicted by aberrant promoter methylation and somatic mutation predict overall survival of late-stage colorectal cancer.Scientific reports · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundGenomics has substantially changed our approach to cancer research. Gene expression profiling, for example, has been utilized to delineate subtypes of cancer, and facilitated derivation of predictive and prognostic signatures. The emergence of technologies for the high resolution and genome-wide description of genetic and epigenetic features has enabled the identification of a multitude of causal DNA events in tumors. This has afforded the potential for large scale integration of genome and transcriptome data generated from a variety of technology platforms to acquire a better understanding of cancer.
resultsHere we show how multi-dimensional genomics data analysis would enable the deciphering of mechanisms that disrupt regulatory/signaling cascades and downstream effects. Since not all gene expression changes observed in a tumor are causal to cancer development, we demonstrate an approach based on multiple concerted disruption (MCD) analysis of genes that facilitates the rational deduction of aberrant genes and pathways, which otherwise would be overlooked in single genomic dimension investigations.
conclusionsNotably, this is the first comprehensive study of breast cancer cells by parallel integrative genome wide analyses of DNA copy number, LOH, and DNA methylation status to interpret changes in gene expression pattern. Our findings demonstrate the power of a multi-dimensional approach to elucidate events which would escape conventional single dimensional analysis and as such, reduce the cohort sample size for cancer gene discovery.
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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.