Prioritizing Cancer Genes Based on an Improved Random Walk Method

Wei, Pi-Jing; Wu, Fang‐Xiang; Xia, Junfeng; Su, Yansen; Wang, Jing; Zheng, Chun-Hou

doi:10.3389/fgene.2020.00377

Cited by 25 publications

(17 citation statements)

References 57 publications

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“…We therefore applied network-based approach Driver_IRW to identify genetic drivers of each individual aneurysm subtype. Driver_IRW is a novel computational method, of which the basic assumption is that, in the interaction network, genes with higher degree have higher possibility to transit from upstream seed nodes [ 11 ]. Thus, genes with higher final random walk scores are more likely to be disease driver genes.…”

Section: Resultsmentioning

confidence: 99%

“…To predict the most promising driver genes of various aneurysm subtypes, a state-of-the-art random-walk-based approach Driver_IRW was employed by using the R package ‘Pijing09/Driver_IRW’ [ 11 ]. Driver_IRW was developed to identify and prioritize driver genes which contribute to disease pathology based on transcriptome profile and interaction network.…”

Section: Methodsmentioning

confidence: 99%

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Comparative analysis of aneurysm subtypes associated genes based on protein–protein interaction network

2021

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The arterial aneurysm refers to localized dilation of blood vessel wall and is common in general population. The majority of aneurysm cases remains asymptomatic until a sudden rupture which is usually fatal and of extremely high mortality (~ 50–60%). Therefore, early diagnosis, prevention and management of aneurysm are in urgent need. Unfortunately, current understanding of disease driver genes of various aneurysm subtypes is still limited, and without appropriate biomarkers and drug targets no specialized drug has been developed for aneurysm treatment. In this research, aneurysm subtypes were analyzed based on protein–protein interaction network to better understand aneurysm pathogenesis. By measuring network-based proximity of aneurysm subtypes, we identified a relevant closest relationship between aortic aneurysm and aortic dissection. An improved random walk method was performed to prioritize candidate driver genes of each aneurysm subtype. Thereafter, transcriptomes of 6 human aneurysm subtypes were collected and differential expression genes were identified to further filter potential driver genes. Functional enrichment of above driver genes indicated a general role of ubiquitination and programmed cell death in aneurysm pathogenesis. Especially, we further observed participation of BCL-2-mediated apoptosis pathway and caspase-1 related pyroptosis in the development of cerebral aneurysm and aneurysmal subarachnoid hemorrhage in corresponding transcriptomes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparative analysis of aneurysm subtypes associated genes based on protein–protein interaction network

2021

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“…We may need to improve MinNetRank from two aspects in the future. On one hand, we could integrate the gene co-expression network with the interaction networks ( Hou et al, 2019 ; Wei et al, 2020 ). We also need to incorporate additional types of omics data (genomics, transcriptomics, proteomics, epigenomics, and images).…”

Section: Discussionmentioning

confidence: 99%

“…It is desirable to use incoming and outgoing degree simultaneously. Driver_IRW (Driver genes discovery with Improved Random Walk method) assigns different transition probabilities for different genes of the interaction network ( Wei et al, 2020 ). DeepDriver predicts cancer driver genes based on mutation-based features and gene similarity networks using deep convolutional neural networks ( Luo et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Wei

Luo

et al. 2021

Front. Genet.

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Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

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“…Topological properties such as node degree (number of edges connected to the node) are known for their importance in network organization and playing as central hubs in orchestrating molecular connections [67]. It is reported that cancer-associated proteins have large betweenness centrality as they control the communication between different components of a network [68]. Among non-topological properties, we have calculated the involvement of the motif constituents in the disease pathway, the gene prioritization score, and average Log2 fold change for each motif based on the change in expression values of each node from noninvasive to invasive phenotypes derived from in vitro experiments.…”

Section: Discussionmentioning

confidence: 99%

Logic-Based Modeling and Virtual Drug Screening for the Prediction of Novel Therapeutic Targets and Combination Regimens Against E2F1-driven Melanoma Progression

Singh¹,

Khan²,

Bala³

et al. 2021

Preprint

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Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream pro-metastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds potential to prevent metastasis, however these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which challenge the characterization of drug targetablemake it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and comprehensively identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identifiedy that simultaneous perturbation of AKT1 and MDM2 drastically reduces EMT in metastatic melanoma. Using the structures of the two protein signatures along with virtual screening of lead-like compound library available in ZINC12 database, we identified a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. We propose that these compounds could be taken into account in the design of novel therapeutic strategies for the management of aggressive melanoma. were identified using virtual screening of lead-like compound library available in ZINC12 database. Subsequent high-throughput virtual screening of drug library using the structures of the two protein signatures predicted a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. These can be experimentally evaluated and further considered as new anti-melanoma metastatic agents, in monotherapies or combination regimens.

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Prioritizing Cancer Genes Based on an Improved Random Walk Method

Cited by 25 publications

References 57 publications

Comparative analysis of aneurysm subtypes associated genes based on protein–protein interaction network

Comparative analysis of aneurysm subtypes associated genes based on protein–protein interaction network

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Logic-Based Modeling and Virtual Drug Screening for the Prediction of Novel Therapeutic Targets and Combination Regimens Against E2F1-driven Melanoma Progression

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