Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data

Zhang, Tianjiao; Hu, Yang; Wu, Xiaoliang; Ma, Rui; Jiang, Qinghua; Wang, Yadong

doi:10.1155/2016/2395341

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…Human genetics studies have offered the strongest evidence thus far to connect genes to human diseases. In 2016, Zhang et al [ 10 ] identified 22 liver cancer–related enhancer single-nucleotide polymorphisms by integrating genome-wide association study (GWAS) data and histone modification ChIP-seq data. Sanseau and Agarwal [ 11 ] found that disease genes extracted from GWAS data were 2.7-fold more likely to be drug targets.…”

Section: Introductionmentioning

confidence: 99%

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Wang

Yang

et al. 2021

PLoS Comput Biol

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Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.

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Section: Introductionmentioning

confidence: 99%

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Wang

Yang

et al. 2021

PLoS Comput Biol

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“…Given the fact that experimentally identifying of the complete list of disease-related genes is generally impractical due to the high cost, computational methods have been proposed in the last decades to predict the relationships between genes and human diseases [ 2 – 10 ]. However, these tools, including filtering methods based on a set of criteria [ 11 ], text mining of biomedical literature [ 12 ], integration of genomic data [ 13 – 15 ], semantic similarity [ 16 – 21 ] based disease gene prioritization [ 22 ] and network analysis based and highly robust approach [ 8 , 23 – 26 ], remain pre-eminent [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting disease-related genes using integrated biomedical networks

et al. 2017

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BackgroundIdentifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.ResultsWe propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.ConclusionsThe experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3263-4) contains supplementary material, which is available to authorized users.

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“…According to the number of mutated bases, genomic variations have been classified as: 1) single-nucleotide variations (formerly single-nucleotide polymorphisms); 2) very short insertions and deletions, usually less than 50 bp; and 3) structural variations, usually longer than 50 bp (Genome structural variati, 2011). Gene mutations are known to be closely related to the occurrence and development of diseases ( Hunt et al, 2014 ; Alkan et al, 2011 ; Yin et al, 2020 ; Fang et al, 2019 ; Li et al, 2020a ; He et al, 2020 ; Zhang et al, 2020a ; Zhang et al, 2016 ; Hu et al, 2021 ; Hu et al, 1990 ; Hu et al, 2020 ). High-throughput sequencing technologies have allowed the mutations in the genomes of patients with particular diseases to be determined systematically, quickly and accurately, including common but less studied synonymous mutations in the coding regions of genomes ( Meyerson et al, 2010 ; Li et al, 2017 ; Cheng et al, 2018 ; Zhou et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Genomic Variation Prediction: A Summary From Different Views

Lin

2021

Front. Cell Dev. Biol.

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Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

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Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data

Cited by 9 publications

References 21 publications

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Predicting disease-related genes using integrated biomedical networks

Genomic Variation Prediction: A Summary From Different Views

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