Integrative Analysis of Genome, 3D Genome, and Transcriptome Alterations of Clinical Lung Cancer Samples

Li, Tingting; Li, Ruifeng; Dong, Xuan; Shi, Lin; Lin, Miao; Peng, Ting; Wu, Pengze; Liu, Yuting; Li, Xiaoting; He, Xuheng; Han, Xu; Kang, Bin; Wang, Yinan; Liu, Zhiheng; Chen, Qing; Shen, Yue; Feng, Mingxiang; Wang, Xiangdong; Wu, Duojiao; Wang, Jian; Cheng, Li

doi:10.1016/j.gpb.2020.05.007

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…The three-dimensional (3D) architecture of chromatin is essential for gene expression during cell differentiation and disease development [1,2]. Recent advances in next generation sequencing have led to the development of several chromosome conformation capture techniques, such as Hi-C, Micro-C, and Pore-C [3][4][5], enabling the exploration of multiscale chromatin structural elements including chromatin compartment [3,6], topological associating domains (TADs) [7,8], loops [6], stripes [9] and microcompartments [10].…”

Section: Introductionmentioning

confidence: 99%

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

Li,

Zhang,

et al. 2024

Preprint

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Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using COnvolution and attention mechanisms to infer fine-scale chromatin COmpartment pAtterns from six histone modification signals. COCOA extracts 1-D track features through bi-directional feature reconstruction after resolution-specific binning epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed with 1kb resolution high-depth experimental data, COCOA generates clear and detailed compartmental patterns, highlighting its superior performance. Finally, we demonstrated that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining chromatin compartmentalization insights from epigenomics in diverse biological scenarios.

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Section: Introductionmentioning

confidence: 99%

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

Li,

Zhang,

et al. 2024

Preprint

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“…The 3D architecture of the genome is altered in many cancers [10] , [11] , [12] , [13] . Recent research used high-throughput chromosome conformation capture (Hi-C) to characterize the larger-scale 3D genomic structures in lung cancer [14] . Therefore, clarifying 3D genomic disorders in cancer cells may be an innovative way to conduct cancer research.…”

Section: Introductionmentioning

confidence: 99%

Mapping Multi-Factor-Mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-Related Genes

Zhang

et al. 2023

Genomics, Proteomics &Amp; Bioinformatics

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Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

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“…Therefore, clarifying 3D genomic disorders in cancer cells may be an innovative way to conduct cancer research. A recent study used Hi-C 5 to characterize the larger-scale 3D genomic structures of lung cancer [12]. However, 3D information related to gene expression regulation still needs to be more fully studied.…”

Section: Introductionmentioning

confidence: 99%

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

Zhang

zhang

et al. 2022

Preprint

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The study of lung cancer genome is an indispensable basis for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, the dysregulation of long-range chromatin interactions in lung cancer has been poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we generated high-resolution data revealing chromatin interactions associated with RNAPII (RNA Polymerase II), CTCF (CCCTC-binding factor), EZH2 (Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit), and H3K27me3 (Histone 3 lysine 27 trimethylation) by using specific antibodies with the long-read ChIA-PET (Chromatin interaction analysis by paired-end tag sequencing) method. EZH2/H3K27me3-mediated interactions are found to further silence target genes, either through loops or domains, and have distributions along the genome that are distinct from and complementary to those associated with RNAPII. We found that cancer-related genes are highly enriched in chromatin interactions. We identified abnormal interactions on oncogenes and tumor suppressors, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. The knock-out of the abnormal interaction can reverse the dysregulation of cancer-related genes, which suggest that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings provide a 3D landscape and gene regulatory relationship of lung cancer genome.

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Integrative Analysis of Genome, 3D Genome, and Transcriptome Alterations of Clinical Lung Cancer Samples

Cited by 4 publications

References 44 publications

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

Mapping Multi-Factor-Mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-Related Genes

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

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