Diagnosing phenotypes of single-sample individuals by edge biomarkers

Zhang, Wanwei; Zeng, Tao; Li, Xiaoping; Chen, Luonan

doi:10.1093/jmcb/mjv025

Cited by 67 publications

(59 citation statements)

References 45 publications

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“…Biological network information includes only a small number of genes; 20,000~40,000 transcripts are generally observed in the whole transcriptome data but only ~10,000 genes are used in the interaction data. There could be another approaches with de novo network construction with edge probability using statistical approaches8485. As more comprehensive interaction data, including regulatory data, is available, our method can be more accurate in predicting ITH.…”

Section: Discussionmentioning

confidence: 99%

Measuring intratumor heterogeneity by network entropy using RNA-seq data

Park

Lim

Nam

et al. 2016

Sci Rep

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Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

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Section: Discussionmentioning

confidence: 99%

Measuring intratumor heterogeneity by network entropy using RNA-seq data

Park

Lim

Nam

et al. 2016

Sci Rep

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“…To determine a person's state of health, many studies have shown that network-based biomarkers, e.g. subnetwork markers (5,6), network biomarkers (8) and edge biomarkers (9,10), are superior to traditional single-molecule biomarkers for accurately characterizing disease states due to their additional information on interactions and networks. In particular, an individual-specific network is considered to be reliable for accurately characterizing the specific disease state of an individual.…”

Section: Introductionmentioning

confidence: 99%

Personalized characterization of diseases using sample-specific networks

et al. 2016

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A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

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“…In this study, our method was applied to select a panel of biomarkers to improve the diagnosis of CRC. In future we will apply our method to other cancer analysis by further considering dynamical information for dynamical network biomarkers [49][50][51][52][53]69] and network information for network biomarkers [54][55][56][57][58][59] or edge biomarkers [64][65].…”

Section: Discussionmentioning

confidence: 99%