ArticleNucleic acids research2015
Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 43 citations in OpenAlex.
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.Molecular oncology · 2023Article
- RNA modification: mechanisms and therapeutic targets.Molecular biomedicine · 2023Review
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- Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer.BMC cancer · 2021Article
- A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.Biomolecules · 2021Review
- The non-coding epitranscriptome in cancer.Briefings in functional genomics · 2021Review
- Acquisition of chromosome 1q duplication in parental and genome-edited human-induced pluripotent stem cell-derived neural stem cells results in their higher proliferation rate in vitro and in vivo.Cell proliferation · 2020Article
- Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers.Molecular therapy. Nucleic acids · 2020Article
- Role of RNA modifications in cancer.Nature reviews. Cancer · 2020Review
- Quantile Function on Scalar Regression Analysis for Distributional Data.Journal of the American Statistical Association · 2020Article
- Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers.Molecular therapy. Nucleic acids · 2019Article
- Ranking genomic features using an information-theoretic measure of epigenetic discordance.BMC bioinformatics · 2019Article
- A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.PloS one · 2018Article
- Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.BMC systems biology · 2017Article
- Cooperative genomic alteration network reveals molecular classification across 12 major cancer types.Nucleic acids research · 2017Article
- Article
- Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis.Frontiers of medicine · 2016Article
- Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis.Genome medicine · 2016Article
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.
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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.