ArticleScientific reports2016
Identification of novel candidate drivers connecting different dysfunctional levels for lung adenocarcinoma using protein-protein interactions and a shortest path approach.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 28 citations in OpenAlex.
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- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.Frontiers in cell and developmental biology · 2022Article
- Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma.Frontiers in bioengineering and biotechnology · 2020Article
- Investigation of miRNA and mRNA Co-expression Network in Ependymoma.Frontiers in bioengineering and biotechnology · 2020Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer.Oncology letters · 2018Article
- Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links.BMC bioinformatics · 2018Article
- Identification of modules of hepatic encephalopathy based on protein-protein network and gene expression data.Experimental and therapeutic medicine · 2018Article
- Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy.OncoTargets and therapy · 2018Article
- Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer.OncoTargets and therapy · 2018Article
- Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.Genes · 2017Article
- Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein-Protein Interaction Network.International journal of molecular sciences · 2017Article
- Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm.BioMed research international · 2017Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumors are formed by the abnormal proliferation of somatic cells with disordered growth regulation under the influence of tumorigenic factors. Recently, the theory of "cancer drivers" connects tumor initiation with several specific mutations in the so-called cancer driver genes. According to the differentiation of four basic levels between tumor and adjacent normal tissues, the cancer drivers can be divided into the following: (1) Methylation level, (2) microRNA level, (3) mutation level, and (4) mRNA level. In this study, a computational method is proposed to identify novel lung adenocarcinoma drivers based on dysfunctional genes on the methylation, microRNA, mutation and mRNA levels. First, a large network was constructed using protein-protein interactions. Next, we searched all of the shortest paths connecting dysfunctional genes on different levels and extracted new candidate genes lying on these paths. Finally, the obtained candidate genes were filtered by a permutation test and an additional strict selection procedure involving a betweenness ratio and an interaction score. Several candidate genes remained, which are deemed to be related to two different levels of cancer. The analyses confirmed our assertions that some have the potential to contribute to the tumorigenesis process on multiple levels.
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