What are the newest methods of lung cancer driver gene identifiation?
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The latest methods for identifying lung cancer driver genes involve utilizing various omics levels and network approaches. One approach integrates epigenomics, genomics, transcriptomics, and post-transcriptomics data to identify driver genes at different levels and uses the Laplacian heat diffusion algorithm on protein-protein interaction networks to discover latent driver genes . Another method focuses on epigenetically regulated genes by analyzing promoter-level expression profiles and DNA methylation data, identifying a core set of coding genes and long noncoding RNAs that are upregulated in non-small cell lung cancer due to promoter hypomethylation . Additionally, a network approach involving transcriptional regulatory networks and algorithms based on centrality, mediocrity, and proximity parameters is used to pinpoint influential genes associated with lung cancer .
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| Papers (4) | Insight |
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03 Apr 2023 | The paper identifies epigenetically regulated genes in NSCLC using integrative CAGE and DNA methylation profiling, presenting a robust list of 22 candidate driver genes that complement known mutational drivers. |
23 Feb 2023 | The paper proposes a method using transcriptional regulation networks and network analysis to predict driver genes and mutations in lung cancer genomes, offering a novel approach for identification. |
| The paper discusses the use of the Oncomine Dx Target Test Multi-CDx system as a method for identifying actionable driver gene variants in lung cancer. | |
2 Citations | The paper describes a computational investigation using the Laplacian heat diffusion algorithm to identify novel lung cancer driver genes involving multiple omics levels. |
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