A Novel Approach to Identify the miRNA-mRNA Causal Regulatory Modules in Cancer

Luo, Jiawei; Huang, Wei; Cao, Buwen

doi:10.1109/tcbb.2016.2612199

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…Optimal IDA has been implemented in the R package pcalg. While causal search methods in general have some well-known shortcomings, IDA has proved to be a valuable tool for instance for screening purposes in large datasets (Le et al, 2013;Engelmann et al, 2015;Luo et al, 2018). The 'optimal' version can further improve its performance.…”

Section: Discussionmentioning

confidence: 99%

On efficient adjustment in causal graphs

Witte,

Henckel,

Maathuis

et al. 2020

Preprint

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We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are, however, not unique. Recent research has introduced a graphical criterion for an 'optimal' valid adjustment set (O-set). For a given graph, adjustment by the O-set yields the smallest asymptotic variance compared to other adjustment sets in certain parametric and non-parametric models. In this paper, we provide three new results on the O-set. First, we give a novel, more intuitive graphical characterisation: We show that the O-set is the parent set of the outcome node(s) in a suitable latent projection graph, which we call the forbidden projection. An important property is that the forbidden projection preserves all information relevant to total causal effect estimation via covariate adjustment, making it a useful methodological tool in its own right. Second, we extend the existing IDA algorithm to use the O-set, and argue that the algorithm remains semi-local. This is implemented in the R-package pcalg. Third, we present assumptions under which the O-set can be viewed as the target set of popular non-graphical variable selection algorithms such as stepwise backward selection.

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Section: Discussionmentioning

confidence: 99%

On efficient adjustment in causal graphs

Witte,

Henckel,

Maathuis

et al. 2020

Preprint

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“…MicroRNA (miRNA) is a cluster of newly discovered non-encoding RNA molecules with 21-23 nucleotides long. 7,8 They are transcribed by RNA polymerase II and play important roles in development of various human diseases, such as the tumorigenesis of human cancers. 9,10 A large number of studies have shown that aberrant miRNA expression is casual in various human cancers.…”

Section: Introductionmentioning

confidence: 99%

MiR-557 works as a tumor suppressor in human lung cancers by negatively regulating LEF1 expression

et al. 2017

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MicroRNAs play an important role in regulating post-transcriptional gene expression in the progression of various human cancers. In this study, we investigated the role of microRNA-557 in human lung cancer cells. The molecular mechanism of microRNA-557 was also clarified in the proliferation and invasion of human lung cancer cells. Our results showed microRNA-557 levels were obviously decreased in clinical lung cancer specimens and lung cancer cell lines. Cell viability of A549 and NCI-H460 cells transfected with microRNA-557 mimics was significantly decreased than those transfected with negative control mimics. MicroRNA-557 promoted cell death of A549 and NCI-H460 but did not affect the cell apoptosis of lung cancer cells. Overexpression of microRNA-557 inhibited cell invasion of A549 and NCI-H460 cells. TargetScan analysis showed that microRNA-557 might target 3' untranslated region of lymphocyte enhancement factor 1, and the western blotting results showed that transfection of microRNA-557 mimics significantly decreased the levels of lymphocyte enhancement factor 1 in A549 and H460 cells. MicroRNA-557 might work as a tumor suppressor by negatively regulating the expression of lymphocyte enhancement factor 1 in lung cancer cells.

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“…The causal relationships between miRNAs and their target genes have been explored in [ 17 ] for MRMs discovery, which has been neglected by the other methods. Statistical correlation and causal interaction between both the biomarkers are simultaneously explored by CALM to identify biologically significant MRMs.…”

Section: Computational Approaches For Mrms Identificationmentioning

confidence: 99%

A review on methods for predicting miRNA–mRNA regulatory modules

Madhumita

Paul

2022

Journal of Integrative Bioinformatics

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Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

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A Novel Approach to Identify the miRNA-mRNA Causal Regulatory Modules in Cancer

Cited by 12 publications

References 27 publications

On efficient adjustment in causal graphs

On efficient adjustment in causal graphs

MiR-557 works as a tumor suppressor in human lung cancers by negatively regulating LEF1 expression

A review on methods for predicting miRNA–mRNA regulatory modules

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