Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network

Abstract

BackgroundCancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance.MethodsConsidering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN).ResultsThis method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes.ConclusionThe final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Identification and Segregation of Genes with Improved Recurrent Neural Network Trained with Optimal Gene Level and Mutation Level Features

“…In 2019, Song et al [6] developed DyTidriver, a new approach that incorporates gene dysregulated expression, tissue-specific appearance, and variability rate into the humans functioning interactions network to identify driver genes (e.g. human FIN).…”

Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism

“…However, these network-based methods are limited by the reliability of the PPI network. To improve PPI reliability and to fully use the valuable multi-omics data, some methods use multi-omics data to weight PPI and filter out the noisy connections under the constraint of co-expression, co-subcellular and co-tissue [9,10] among the molecules. However, these methods do not efficiently exploit the relationships between multi-omics data to boost the accuracy of the driver gene prediction.…”

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver

“…The genes that are highly connected with other genes in the network, called "hub" genes, are expected to be important in pathology (7). As such, many pipelines discovering driver genes incorporate information from coexpression networks and these hub genes into the next phase of multiomics approaches (8)(9)(10). It has, however, been noted that hub genes are not stable, and they are not guaranteed to be driver genes (11).…”