Identification of super enhancer-associated key genes for prognosis of germinal center B-cell type diffuse large B-cell lymphoma by integrated analysis

Li, Xi; Duan, Yan; Hao, Yuxia

doi:10.1186/s12920-021-00916-z

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…Super-enhancers (SEs) are a large cluster of active enhancers critical for maintaining cell identity and driving the expression of some oncogenes ( Kai et al, 2021 ; Zhou et al, 2021 ). However, the previous studies had rarely constructed a risk prediction model based on SE-associated hub genes ( Li, Duan and Hao, 2021 ). In this study, we succeeded in building a superior polygenic prognostic model by analyzing the data of the DLBCL patients from the GEO database, taking some clinical indicators into account as well, which was also rare in previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…The loss or gain of SEs has been reported in various tumors ( He et al, 2019 ); similarly, SEs play a key role in the progression of DLBCL by activating the expression of downstream oncogenes ( Chapuy et al, 2013 ). In addition, SE inhibitors (JQ1) used to treat DLBCL suppress the expression of these genes ( Li et al, 2021 ). Therefore, the exploitation and identification of SEs-driven hub oncogenes will provide novel insights into the diagnosis, prognosis, and treatment of DLBCL.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

Jiang

et al. 2022

Front. Genet.

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Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease that can have profound differences in survival outcomes. A variety of powerful prognostic factors and models have been constructed; however, the development of more accurate prognosis prediction and targeted treatment for DLBCL still faces challenges. An explosion of research on super-enhancer (SE)–associated genes provide the possibility to use in prognostication for cancer patients. Here, we aimed to establish a novel effective prognostic model using SE-associated genes from DLBCL.Methods: A total of 1,105 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. A total of 11 SE-associated genes (BCL2, SPAG16, PXK, BTG1, LRRC37A2, EXT1, TGFBR2, ANKRD12, MYCBP2, PAX5, and MYC) were initially screened and identified by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis. Finally, a risk score model based on these 11 genes was constructed.Results: Kaplan–Meier (K–M) curves showed that the low-risk group appeared to have better clinical survival outcomes. The excellent performance of the model was determined via time-dependent receiver operating characteristic (ROC) curves. A nomogram based on the polygenic risk score was further established to promote reliable prognostic prediction. This study proposed that the SE-associated-gene risk signature can effectively predict the response to chemotherapy in DLBCL patients.Conclusion: A novel and reliable SE-associated-gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of overall survival was developed, which may assist clinicians in the treatment of DLBCL.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

Jiang

et al. 2022

Front. Genet.

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“…ADNP overexpression has also been linked to the pathogenesis of germinal center B cell-type diffuse large B cell lymphoma (GCB-DLBCL); genome-wide screening of cancer-associated super-enhancers in this cell type and other human B cell lymphoma lines revealed the upregulation of ADNP , which is significantly associated with increased risk of death. Moreover, ADNP knockdown in GCB subtypes of B cell lymphoma inhibited proliferation, overall suggesting that epigenetic super-enhancer-mediated control of ADNP contributes to GCB-DLBCL progression [ 164 ]. ADNP is also part of a protein hub that is enriched in the signal transduction of pathways involved in colorectal cancer (Rahman et al, 2019).…”

Section: Adnp Dysregulation In Cancer Developmentmentioning

confidence: 99%

Chromatin remodeler Activity-Dependent Neuroprotective Protein (ADNP) contributes to syndromic autism

et al. 2023

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Background Individuals affected with autism often suffer additional co-morbidities such as intellectual disability. The genes contributing to autism cluster on a relatively limited number of cellular pathways, including chromatin remodeling. However, limited information is available on how mutations in single genes can result in such pleiotropic clinical features in affected individuals. In this review, we summarize available information on one of the most frequently mutated genes in syndromic autism the Activity-Dependent Neuroprotective Protein (ADNP). Results Heterozygous and predicted loss-of-function ADNP mutations in individuals inevitably result in the clinical presentation with the Helsmoortel–Van der Aa syndrome, a frequent form of syndromic autism. ADNP, a zinc finger DNA-binding protein has a role in chromatin remodeling: The protein is associated with the pericentromeric protein HP1, the SWI/SNF core complex protein BRG1, and other members of this chromatin remodeling complex and, in murine stem cells, with the chromodomain helicase CHD4 in a ChAHP complex. ADNP has recently been shown to possess R-loop processing activity. In addition, many additional functions, for instance, in association with cytoskeletal proteins have been linked to ADNP. Conclusions We here present an integrated evaluation of all current aspects of gene function and evaluate how abnormalities in chromatin remodeling might relate to the pleiotropic clinical presentation in individual“s” with Helsmoortel–Van der Aa syndrome.

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Section: Introductionmentioning

confidence: 99%

“…SEs have also been identified using the HOMER bioinformatics analysis suite ( Table 2 ) [ 78–80 ]. According to the HOMER-based approach, SEs are defined based on the elevated local sequence read density of such genomic regions, where the individual ChIP-seq peaks of key transcription factors are closer to each other than 12.5 kb [ 78 ]. Each ChIP-seq peak or group of peaks is given a read density score, the sorting based on the score value, and those located on a region of graph slope > 1 are defined as SEs [ 78 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of super-enhancer using machine learning and its application to medical biology

Hamamoto

Takasawa

Shinkai

et al. 2023

Briefings in Bioinformatics

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The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.

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Identification of super enhancer-associated key genes for prognosis of germinal center B-cell type diffuse large B-cell lymphoma by integrated analysis

Cited by 8 publications

References 34 publications

A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

Chromatin remodeler Activity-Dependent Neuroprotective Protein (ADNP) contributes to syndromic autism

Analysis of super-enhancer using machine learning and its application to medical biology

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