Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

Li, Yue; Liang, Minggao; Zhang, Zhaolei

doi:10.1371/journal.pcbi.1003908

Cited by 66 publications

(87 citation statements)

References 75 publications

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“…118 Several reports comparing the contributions of miRNA regulation and DNA methylation in gene expression conclude that methylation, which is known to be implicated in the fine-tuning of gene expression, is much more important than miRNA regulation. 119 Thus, drug-induced reduction of DNA methylation is a promising therapeutic strategy for human cancers.…”

Section: Therapeutic Strategiesmentioning

confidence: 99%

Mutual regulation of microRNAs and DNA methylation in human cancers

Wang¹,

Wu²,

Claret

2017

Epigenetics

124

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microRNAs (miRNAs) and DNA methylation are the 2 epigenetic modifications that have emerged in recent years as the most critical players in the regulation of gene expression. Compelling evidence has indicated the roles of miRNAs and DNA methylation in modulating cellular transformation and tumorigenesis. miRNAs act as negative regulators of gene expression and are involved in the regulation of both physiologic conditions and during diseases, such as cancer, inflammatory diseases, and psychiatric disorders, among others. Meanwhile, aberrant DNA methylation manifests in both global genome changes and in localized gene promoter changes, which influences the transcription of cancer genes. In this review, we described the mutual regulation of miRNAs and DNA methylation in human cancers. miRNAs regulate DNA methylation by targeting DNA methyltransferases or methylation-related proteins. On the other hand, both hyper-and hypo-methylation of miRNAs occur frequently in human cancers and represent a new level of complexity in gene regulation. Therefore, understanding the mechanisms underlying the mutual regulation of miRNAs and DNA methylation may provide helpful insights in the development of efficient therapeutic approaches.

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Section: Therapeutic Strategiesmentioning

confidence: 99%

Mutual regulation of microRNAs and DNA methylation in human cancers

Wang¹,

Wu²,

Claret

2017

Epigenetics

124

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“…As one challenge, the experimental condition of public ChIP-seq data, such as stem cell line, may not match the physiological condition of a specific cancer type. Even though analysis can be done between ChIP-seq data and cancer type with similar conditions (13), it remains to be seen how to use most public ChIP-seq profiles across diverse cancer types. Meanwhile, the cancer genome is highly unstable, and the gene expression change could arise from CNAs not under the direct effect of TF regulation (14).…”

mentioning

confidence: 99%

Inference of transcriptional regulation in cancers

Jiang

Freedman

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancerassociated TFs, we comprehensively integrated 686 ENCODE ChIPseq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNAbinding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3′UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.regulatory inference | tumor profiling | transcription factor | RNA-binding proteinT umorigenesis is a multistep process requiring alterations in gene expression programs (1, 2). Transcription factors (TFs) are instrumental in driving these gene expression programs, and misregulation of these TFs can result in the acquisition of tumorrelated properties (3). For example, E2F1 is overexpressed in many cancer types and promotes tumor proliferation by regulating expression of genes involved in cell differentiation, metabolism, and development (4). As another example, FOXM1 plays an important role in promoting cell proliferation and cell cycle progression through transcriptional activation of many G2/M-specific genes. Increased FOXM1 gene expression was detected in numerous cancer types, and FOXM1 is a promising therapeutic target for cancer treatment (5). TFs also play critical roles in inducing the tumor microenvironment for metastasis. For example, SNAI1/2, TWIST, and ZEB1/2 orchestrate the expression of genes involved in cell polarity, cell-cell contact, cytoskeleton structure, and extracellular matrix degradation. The joint effect of these TFs promotes cancer cell motility and invasion in the metastatic process (2, 6).With the rapid development of high-throughput technologies, large amounts of datasets have been generated for regulatory proteins. For example, the ENCODE project generated 689 C...

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“…This framework is presented first; it should be understood as a least common denominator, not as a proper method for network inference by itself. We then describe five recently published methods for genome-wide TF activity estimation as extensions or constraints to this general framework, namely the approach by Schacht et al [31] (estimation of TF activity by the effect on their target genes), RACER [32], RABIT [33], ISMARA [34] and biRte [35]. Additionally, we contrast these more comprehensive methods with the local inference algorithm ARACNE [30], a popular tool for the de-novo reconstruction of gene regulatory networks.…”

Section: Methodsmentioning

confidence: 99%

Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization

2017

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BackgroundGene regulation is one of the most important cellular processes, indispensable for the adaptability of organisms and closely interlinked with several classes of pathogenesis and their progression. Elucidation of regulatory mechanisms can be approached by a multitude of experimental methods, yet integration of the resulting heterogeneous, large, and noisy data sets into comprehensive and tissue or disease-specific cellular models requires rigorous computational methods. Recently, several algorithms have been proposed which model genome-wide gene regulation as sets of (linear) equations over the activity and relationships of transcription factors, genes and other factors. Subsequent optimization finds those parameters that minimize the divergence of predicted and measured expression intensities. In various settings, these methods produced promising results in terms of estimating transcription factor activity and identifying key biomarkers for specific phenotypes. However, despite their common root in mathematical optimization, they vastly differ in the types of experimental data being integrated, the background knowledge necessary for their application, the granularity of their regulatory model, the concrete paradigm used for solving the optimization problem and the data sets used for evaluation.ResultsHere, we review five recent methods of this class in detail and compare them with respect to several key properties. Furthermore, we quantitatively compare the results of four of the presented methods based on publicly available data sets.ConclusionsThe results show that all methods seem to find biologically relevant information. However, we also observe that the mutual result overlaps are very low, which contradicts biological intuition. Our aim is to raise further awareness of the power of these methods, yet also to identify common shortcomings and necessary extensions enabling focused research on the critical points.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0419-z) contains supplementary material, which is available to authorized users.

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Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

Cited by 66 publications

References 75 publications

Mutual regulation of microRNAs and DNA methylation in human cancers

Mutual regulation of microRNAs and DNA methylation in human cancers

Inference of transcriptional regulation in cancers

Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization

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