ArticleNature communications2020
Interpreting pathways to discover cancer driver genes with Moonlight.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 105 citations in OpenAlex.
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- Uncertainty-aware graph structure optimization with ensemble learning for enhanced cancer gene identification.Cell reports methods · 2026Article
- Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes.Molecular oncology · 2025Article
- Knockdown of HGH1 in breast cancer cell lines can inhibit the viability, invasion and migration of tumor cells.Cell adhesion & migration · 2025Article
- 3D-bioprinted liver cancer models derived from primary hepatocytes for simulating cancer initiation and drug screening.Hepatology communications · 2025Article
- DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes.Briefings in bioinformatics · 2025Article
- Dynamic clustering of genomics cohorts beyond race, ethnicity-and ancestry.BMC medical genomics · 2025Article
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- Association between dietary fructose and human colon DNA methylation: implication for racial disparities in colorectal cancer risk using a cross-sectional study.The American journal of clinical nutrition · 2025Article
- Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.Translational oncology · 2025Article
- Comprehensive Analysis of Multi-Omics Data on RNA Polymerase as an Adverse Factor in Head and Neck Squamous Cell Carcinoma.Journal of inflammation research · 2025Article
- MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations.Nature communications · 2024Article
- GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network.iScience · 2024Article
- Precision cancer classification using liquid biopsy and advanced machine learning techniques.Scientific reports · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Extrachromosomal circular DNA orchestrates genome heterogeneity in urothelial bladder carcinoma.Theranostics · 2024Article
- KLF12 transcriptionally regulates PD-L1 expression in non-small cell lung cancer.Molecular oncology · 2023Article
- Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity.Cell reports. Medicine · 2023Article
- A workflow to study mechanistic indicators for driver gene prediction with Moonlight.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
17 authors at 9 institutions in 7 countries.
Funding
Abstract
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.
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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.