ArticleEuropean cytokine network2013
Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis.
Article in European cytokine network, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Early activation of inflammatory pathways in UBA1-mutated hematopoietic stem and progenitor cells in VEXAS.Cell reports. Medicine · 2023Article
- Sequencing of RNA in single cells reveals a distinct transcriptome signature of hematopoiesis in GATA2 deficiency.Blood advances · 2020Article
- WIPER: Weighted in-Path Edge Ranking for biomolecular association networks.Quantitative biology (Beijing, China) · 2019Article
- Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts.Scientific reports · 2017Article
- Microarray‑based screening of differentially expressed genes in glucocorticoid‑induced avascular necrosis.Molecular medicine reports · 2017Article
- MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.BioMed research international · 2017Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Microarrays do not yield direct evidence for functional connections between genes. However, transcription factors (TFs) and their binding sites (TFBSs) in promoters are important for inducing and coordinating changes in RNA levels, and thus represent the first layer of functional interaction. Similar to genes, TFs act only in context, which is why a TF/TFBS-based promoter analysis of genes needs to be done in the form of gene(TF)-gene networks, not individual TFs or TFBSs. In addition, integration of the literature and various databases (e.g. GO, MeSH, etc) allows the adding of genes relevant for the functional context of the data even if they were initially missed by the microarray as their RNA levels did not change significantly. Here, we outline a TF-TFBSs network-based strategy to assess the involvement of transcription factors in agonist signaling and demonstrate its utility in deciphering the response of human microvascular endothelial cells (HMEC-1) to leukemia inhibitory factor (LIF). Our strategy identified a central core of eight TFs, of which only STAT3 had previously been definitively linked to LIF in endothelial cells. We also found potential molecular mechanisms of gene regulation in HMEC-1 upon stimulation with LIF that allow for the prediction of changes of genes not used in the analysis. Our approach, which is readily applicable to a wide variety of expression microarray and next generation sequencing RNA-seq results, illustrates the power of a TF-gene networking approach for elucidation of the underlying biology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.