Fulminant anaplastic large cell lymphoma (ALCL) concomitant with primary cytomegalovirus (CMV) infection, and human herpes virus 8 (HHV-8) infection together with Epstein-Barr-virus (EBV) reactivation in a patient with asymptomatic HIV-infection

Grützmeier, Sven; Porwit, Anna; Schmitt, Corinna; Sandström, Eric; Åkerlund, Börje; Ernberg, Ingemar

doi:10.1186/s13027-016-0094-5

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Many viruses such as HBV, HIV, CMV and HHV have been observed to generate Lnc RNAs to affect transcription, antiviral responses, RNA stability, metabolism and viral pathogenesis in host cells. 82 In addition, it has been shown viral proteins deregulate host cell Lnc RNAs in response to viral replication or antiviral response. 83 Hepatitis B and C can cause acute or chronic infection in the liver which could be followed by cirrhosis, liver failure and eventually hepatocellular carcinoma (HCC) in some cases with chronic infection.…”

Section: Lnc Rnas In Bacterial Cancersmentioning

confidence: 99%

Long non‐coding RNAs and microorganism‐associated cancers

Ranjbar

Behjatfar

Teimouri

et al. 2021

Cell Biochemistry & Function

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Cancerous cells are abnormal cells characterized by aberrant growth and proliferation, which can involve various types of cells and tissues. Through numerous signalling pathways, many mechanisms are involved in cells that keep them normal. These signalling pathways are tightly set by different proteins whose expression is regulated by a large number of factors. In other words, when a regulating factor does not act properly or undergoes a change in its function or expression, the result will be that the subordinate gene and subsequently the related protein will show deranged expression and activity. This leads to disordered signalling pathways which bring about uncontrolled proliferation in cells. One of the most significant factors in adjusting the expression of genes is noncoding RNAs. It should be noted that all underlying causes initiating malignancy try to alter the main regulatory factors in cellular processes and gene expression and direct the cell to an unregulated state.Microorganisms have been identified as one of the important elements to direct normal cells to abnormality. That is, they probably agitate the malignant traits through manipulating significant factors such as ncRNAs in given cells using their own or host-related factors. The present study is aimed at examining how the long noncoding RNAs are involved in microorganism-mediated cancers.

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Section: Lnc Rnas In Bacterial Cancersmentioning

confidence: 99%

Long non‐coding RNAs and microorganism‐associated cancers

Ranjbar

Behjatfar

Teimouri

et al. 2021

Cell Biochemistry & Function

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Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Li,

Ma,

Wang

et al. 2024

Briefings in Bioinformatics

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Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF–enhancer linkage prediction, and enhancer–gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer–gene relations associated with pseudotime, as well as key TF-enhancer–gene relations and TF cooperation underlying tumor cells.

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Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Wang

et al. 2022

Preprint

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We present an algorithm, STREAM, for enhancer-driven gene regulatory network (eGRN) inference from transcriptome and chromatin accessibility profiled in the same cells. The algorithm improves the prediction accuracy of relations among transcription factors (TFs), enhancers, and genes by two new ideas: (i) a Steiner forest problem model to identify the set of highly confident enhancer-gene relations underlying a context-specific functional gene module; and (ii) a hybrid biclustering pipeline integrated with submodular optimization for inferring eGRNs by identifying the optimal set of hybrid biclusters, each of which represents co-regulated genes by the same TF and co-accessible enhancers bound by the same TF over a cell subpopulation. These two ideas are embedded in an iterative framework by finding patterns in a pair of transcriptome and chromatin accessibility matrices. Benchmarking analysis shows that the performance, assessed by f-scores, precision, or recall, was significantly improved by our program compared to four state-of-the-art tools on nine datasets. Besides, by implementing STREAM on an Alzheimer's disease dataset, we identified TF-enhancer-gene relations associated with pseudotime and investigated the changing of enhancer-gene relations alongside cell lineages. Additionally, by implementing STREAM on a diffuse small lymphocytic lymphoma dataset, we excavated key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

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Fulminant anaplastic large cell lymphoma (ALCL) concomitant with primary cytomegalovirus (CMV) infection, and human herpes virus 8 (HHV-8) infection together with Epstein-Barr-virus (EBV) reactivation in a patient with asymptomatic HIV-infection

Cited by 4 publications

References 27 publications

Long non‐coding RNAs and microorganism‐associated cancers

Long non‐coding RNAs and microorganism‐associated cancers

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

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