Novel approach to identify putative Epstein-Barr-virus microRNAs regulating host cell genes with relevance in tumor biology and immunology.
Simon Jasinski-BergnerJuliane BlümkeMarcus BauerSaskia Luise SkiebeOfer MandelboimClaudia WickenhauserBarbara SeligerPublished in: Oncoimmunology (2022)
The human Epstein-Barr virus is associated with several human solid and hematopoietic malignancies. However, the underlying molecular mechanisms including virus-encoded microRNAs (miRs), which lead to the malignant transformation of infected cells and immune evasion of EBV-associated tumors, have not yet been characterized. The expression levels of numerous known EBV-specific miRs and their suitability as diagnostic and/or prognostic markers were determined in different human EBV-positive tissues followed by in silico analyses to identify putative EBV-miR-regulated target genes, thereby offering a suitable screening strategy to overcome the limited available data sets of EBV-miRs and their targeted gene networks. Analysis of microarray data sets from healthy human B cells and malignant-transformed EBV-positive B cells of patients with Burkitt's lymphoma revealed statistically significant (p < 0.05) deregulated genes with known functions in oncogenic properties, immune escape and anti-tumoral immune responses. Alignments of in vivo and in silico data resulted in the prediction of putative candidate EBV-miRs and their target genes. Thus, a combinatorial approach of bioinformatics, transcriptomics and in situ expression analyses is a promising tool for the identification of EBV-miRs and their potential targets as well as their eligibility as markers for EBV detection in different EBV-associated human tissue.
Keyphrases
- epstein barr virus
- diffuse large b cell lymphoma
- endothelial cells
- induced pluripotent stem cells
- genome wide
- pluripotent stem cells
- immune response
- poor prognosis
- single cell
- bioinformatics analysis
- gene expression
- electronic health record
- stem cells
- mesenchymal stem cells
- big data
- cell death
- risk assessment
- transcription factor
- artificial intelligence
- machine learning
- inflammatory response
- toll like receptor
- molecular dynamics simulations
- quantum dots
- genome wide analysis
- pi k akt
- real time pcr