CR2Cancer: a database for chromatin regulators in human cancer

Ru, Beibei; Sun, Jie; Tong, Yin; Wong, Ching Ngar; Chandra, Aditi; Tang, Acacia Tsz So; Chow, Larry Ka-Yue; Wun, Wai Lam; Levitskaya, Zarina; Zhang, Jiangwen

doi:10.1093/nar/gkx877

Cited by 27 publications

(19 citation statements)

References 62 publications

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“…The identification of AMPK-regulated networks can be explored using consensus sequence mapping and machine learning in computing environments such as R/Bioconductor computing [ 25 , 73 , 74 , 75 ]. The expression of effected genes and loci and their application of these loci to disease-relevant stimuli can be further explored via cross-referencing with expression profiles housed in the Gene Expression Omnibus (GEO) database, Sequence Read Archive (SRA), Single Nucleotide Polymorphism database (dbSNP), 3D Genome database C (3DGD), and CR2Cancer [ 76 , 77 , 78 , 79 ]. Integration of these datasets can provide a comprehensive picture of the influence AMPK has on epigenetic signaling cascades in addition to their genetic loci and disease-specific regulation ( Figure 7 ).…”

Section: Approaches To Elucidating the Ampk-modulated Epigenetic Lmentioning

confidence: 99%

AMPK: An Epigenetic Landscape Modulator

Gongol

Sari

Bryant

et al. 2018

IJMS

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Activated by AMP-dependent and -independent mechanisms, AMP-activated protein kinase (AMPK) plays a central role in the regulation of cellular bioenergetics and cellular survival. AMPK regulates a diverse set of signaling networks that converge to epigenetically mediate transcriptional events. Reversible histone and DNA modifications, such as acetylation and methylation, result in structural chromatin alterations that influence transcriptional machinery access to genomic regulatory elements. The orchestration of these epigenetic events differentiates physiological from pathophysiological phenotypes. AMPK phosphorylation of histones, DNA methyltransferases and histone post-translational modifiers establish AMPK as a key player in epigenetic regulation. This review focuses on the role of AMPK as a mediator of cellular survival through its regulation of chromatin remodeling and the implications this has for health and disease.

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Section: Approaches To Elucidating the Ampk-modulated Epigenetic Lmentioning

confidence: 99%

AMPK: An Epigenetic Landscape Modulator

Gongol

Sari

Bryant

et al. 2018

IJMS

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“…A list of epigenomic regulators was created from a public curation of epigenomic regulators from publicly available databases and literature. The public databases used included EpiFactors [26], dbEM [27], and CR2Cancer [28]. Collectively, we combined information about proteins modifying the histones, remodeling nucleosome, proteins modifying genetic material, and, in turn, affecting the expression of the gene, histone chaperone, histones, or histone variants.…”

Section: Curated Epigenomic Regulatorsmentioning

confidence: 99%

Prognostic Significance of Dysregulated Epigenomic and Chromatin Modifiers in Cervical Cancer

Paul

Pillai²,

Kumar

2021

Cells

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To broaden the understanding of the epigenomic and chromatin regulation of cervical cancer, we examined the status and significance of a set of epigenomic and chromatin modifiers in cervical cancer using computational biology. We observed that 61 of 917 epigenomic and/or chromatin regulators are differentially upregulated in human cancer, including 25 upregulated in invasive squamous cell carcinomas and 29 in cervical intraepithelial neoplasia 3 (CIN3), of which 14 are upregulated in cervical intraepithelial neoplasia 2 (CIN2). Interestingly, 57 of such regulators are uniquely upregulated in cervical cancer, but not ovarian and endometrial cancers. The observed overexpression of 57 regulators was found to have a prognostic significance in cervical cancer. The collective overexpression of these regulators, as well as its subsets belonging to specific histone modifications and corresponding top ten positively co-overexpressed genes, correlated with reduced survival of patients with high expressions of the tested overexpressed regulators compared to cases with low expressions. Using cell-dependency datasets from human cervical cancer cells, we found that 20 out of 57 epigenomic and chromatin regulators studied here appeared to be essential genes, as the depletion of these genes was accompanied by the loss in cellular viability. In brief, the results presented here provide further insights into the role of epigenomic and chromatin regulators in the oncobiology of cervical cancer and broaden the list of new potential molecules of therapeutic importance.

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“…The participating microRNA is marked with a dark green rectangle. Targets of chromatin regulators in hepatocellular carcinoma (collected from the CR2Cancer database [60]) are marked with dark green edges. Note that epithelial-mesenchymal transition has many more participating RNAs [61] than microRNA-200 of the original network [12].…”

Section: Key Figurementioning

confidence: 99%

Learning of Signaling Networks: Molecular Mechanisms

Csermely

Kunsic

Mendik

et al. 2020

Trends in Biochemical Sciences

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Molecular processes of neuronal learning have been well-described. However, learning mechanisms of non-neuronal cells have not been fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins and prions, signaling cascades, protein translocation, RNAs (microRNA and lncRNA), and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. Highlights--Besides the well-known learning processes of neurons, non-neuronal single cells are able to learn and show a more robust (and often faster) adaptive response when the same stimulus is repeated.--Known examples of cellular learning are sensitization-or habituation-type responses.--Several molecular mechanisms of neuronal learning, such as conformational memory, protein translocation, signaling cascades, microRNAs, lncRNAs and chromatin memory, also participate in learning of non-neuronal, single cells.--We propose that these molecular mechanisms form the integrative memory of signaling networks and display a generalized Hebbian learning process by increasing those edge weights through which the signal has been propagated. Learning of non-neural cells: Adaptive Molecular Responses Observed when the same Stimulus is RepeatedMolecular mechanisms of neuronal learning have been well-established in the past decades [1]. However, we know relatively little about the molecular details of learning mechanisms of non-neuronal cells. We define cellular learning as an adaptive response to a simple stimulus observed when the same stimulus is repeated in a short time -as compared to the duration of the cell cycle of the given cell. We note that it is crucial to discriminate between molecular changes which are indeed adaptive, and those which are fortuitous by-products of other, co-* Correspondence: csermely.peter@med.semmelweis-univ.hu (P. Csermely)

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CR2Cancer: a database for chromatin regulators in human cancer

Cited by 27 publications

References 62 publications

AMPK: An Epigenetic Landscape Modulator

AMPK: An Epigenetic Landscape Modulator

Prognostic Significance of Dysregulated Epigenomic and Chromatin Modifiers in Cervical Cancer

Learning of Signaling Networks: Molecular Mechanisms

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