ArticleNucleic acids research2017
Discovery of cancer common and specific driver gene sets.
Article in Nucleic acids research, 2017. 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, 89 citations in OpenAlex.
- Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types.PLoS computational biology · 2025Article
- Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.Frontiers in immunology · 2024Article
- Whole-exome sequencing reveals the metastatic potential of hepatocellular carcinoma from the perspective of tumor and circulating tumor DNA.Hepatology international · 2023Article
- Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Familial CCM Genes Might Not Be Main Drivers for Pathogenesis of Sporadic CCMs-Genetic Similarity between Cancers and Vascular Malformations.Journal of personalized medicine · 2023Review
- Inferring cancer common and specific gene networks via multi-layer joint graphical model.Computational and structural biotechnology journal · 2023Article
- Identifying network biomarkers of cancer by sample-specific differential network.BMC bioinformatics · 2022Article
- cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines.Briefings in bioinformatics · 2022Article
- Integrated bioinformatics and experiments reveal the roles and driving forces for HSF1 in colorectal cancer.Bioengineered · 2022Article
- Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.Frontiers in microbiology · 2022Article
- Effect of unfolded protein response on the immune infiltration and prognosis of transitional cell bladder cancer.Annals of medicine · 2021Article
- Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning.Scientific reports · 2021Article
- Prioritizing Cancer Genes Based on an Improved Random Walk Method.Frontiers in genetics · 2020Article
- Identification of second primary tumors from lung metastases in patients with esophageal squamous cell carcinoma using whole-exome sequencing.Theranostics · 2020Observational
- Article
- The transcriptome landscape associated with Disrupted-in-Schizophrenia-1 locus impairment in early development and adulthood.Schizophrenia research · 2019Article
- Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.Scientific reports · 2019Article
- Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Article
- Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.Frontiers in genetics · 2019Article
- Identifying Cancer Specific Driver Modules Using a Network-Based Method.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found.
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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.