3D chromatin architecture and transcription regulation in cancer

Deng, Siwei; Feng, Yuliang; Pauklin, Siim

doi:10.1186/s13045-022-01271-x

Cited by 41 publications

(30 citation statements)

References 340 publications

(335 reference statements)

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“…CGMega differs from other methods in three main ways: (1) It leverages a GAT-based neural network across multiple molecular features, rather than single molecular feature, such as gene coexpression (extensively evaluated elsewhere 61 ). Compared to EMOGI 25 , a different multi-omics integration method, for cancer gene prediction, CGMega is advanced in its ability to capture the 3D genome architecture, which has been widely demonstrated as a new perspective for the study of cancer 62 . Multi-omics data integration is effective for avoiding noisy and sparse data, and neural networks are sufficiently powerful to capture the intrinsic relationships among complex data.…”

Section: Discussionmentioning

confidence: 99%

CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection

Li¹,

Han

Sun

et al. 2023

Preprint

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Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leveraged the recent advances of model-agnostic interpretation approach and developed CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We applied CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncovered the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identified 396 candidate AML genes, and observed the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identified patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

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Section: Discussionmentioning

confidence: 99%

CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection

Li¹,

Han

Sun

et al. 2023

Preprint

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“…The 3D property of chromatin plays a vital role in transcriptional regulation (Dileep and Tsai, 2021;Deng et al, 2022). Although histone modifications such as methylation and acetylation have been the main focus of the studies for transcriptional regulation over the past two decades, studies on how chromatin architecture, such as chromatin loops, R-loops, and DNA topology, controls gene expression have been actively conducted in recent years (Kadauke and Blobel, 2009;Kouzine et al, 2014;Al-Hadid and Yang, 2016).…”

Section: Architecture Of Flc Chromatin and Apmentioning

confidence: 99%

Recent advances in the chromatin-based mechanism of FLOWERING LOCUS C repression through autonomous pathway genes

2022

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Proper timing of flowering, a phase transition from vegetative to reproductive development, is crucial for plant fitness. The floral repressor FLOWERING LOCUS C (FLC) is the major determinant of flowering in Arabidopsis thaliana. In rapid-cycling A. thaliana accessions, which bloom rapidly, FLC is constitutively repressed by autonomous pathway (AP) genes, regardless of photoperiod. Diverse AP genes have been identified over the past two decades, and most of them repress FLC through histone modifications. However, the detailed mechanism underlying such modifications remains unclear. Several recent studies have revealed novel mechanisms to control FLC repression in concert with histone modifications. This review summarizes the latest advances in understanding the novel mechanisms by which AP proteins regulate FLC repression, including changes in chromatin architecture, RNA polymerase pausing, and liquid–liquid phase separation- and ncRNA-mediated gene silencing. Furthermore, we discuss how each mechanism is coupled with histone modifications in FLC chromatin.

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“…Organization of the gene regulation is multi-tiered which contribute as target sites to address ER modulation. 28 This study uses ENCODE data base and data mining to review genomic regulatory features present on ER1 gene to illustrate the non-coding regulatory features, enhancers, variants, TFs, and methylation signatures on ER which can contribute to its modulation. In addition, it provides insights to cohesively integrate this information to understand development of endocrine resistance.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatic Evaluation of Features on Cis-regulatory Elements at 6q25.1

Sreekar

Shrestha

2023

Bioinform Biol Insights

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Eukaryotic non-coding regulatory features contribute significantly to cellular plasticity which on aberration leads to cellular malignancy. Enhancers are cis-regulatory elements that contribute to the development of resistance to endocrine therapy in estrogen receptor (ER)-positive breast cancer leading to poor clinical outcome. ER is vital for therapeutic targets in ER-positive breast cancer. Here, we review and report the different regulatory features present on ER with the objective to delineate potential mechanisms which may contribute to development of resistance. The UCSC Genome Browser, data mining, and bioinformatics tools were used to review enhancers, transcription factors (TFs), histone marks, long non-coding RNAs (lncRNAs), and variants residing in the non-coding region of the ER gene. We report 7 enhancers, 3 of which were rich in TF-binding sites and histone marks in a cell line-specific manner. Furthermore, some enhancers contain estrogen resistance variants and sites for lncRNA. Our review speculates putative models suggesting potential aberrations in gene regulation and expression if these regulatory landscapes and assemblies are altered. This review gives an interesting perspective in designing integrated in vitro studies including non-coding elements to study development of endocrine resistance in ER-positive breast cancer.

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3D chromatin architecture and transcription regulation in cancer

Cited by 41 publications

References 340 publications

CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection

CGMega: Explainable Graph Neural Network Framework with Attention Mechanisms for Cancer Gene Module Dissection

Recent advances in the chromatin-based mechanism of FLOWERING LOCUS C repression through autonomous pathway genes

Bioinformatic Evaluation of Features on Cis-regulatory Elements at 6q25.1

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