ArticlePloS one2014
Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
- Information theory: A foundation for complexity science.Proceedings of the National Academy of Sciences of the United States of America · 2022Review
- Combined proteomics/miRNomics of dendritic cell immunotherapy-treated glioblastoma patients as a screening for survival-associated factors.NPJ vaccines · 2020Article
- Evaluation Expression of Microrna-93 and Integrin Β8 in Different Types of Glioma TumorsAsian Pacific journal of cancer prevention : APJCP · 2017Article
- Diagnosis and New Treatment Modalities for Glioblastoma: Do They Improve Patient Survival?Current molecular medicine · 2016Article
- Serum microRNAs as potential noninvasive biomarkers for glioma.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016Review
- MicroRNA as potential biomarkers in Glioblastoma.Journal of neuro-oncology · 2015Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.
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