Epstein-Barr virus microRNA BART10-3p promotes dedifferentiation and proliferation of nasopharyngeal carcinoma by targeting ALK7

Luo, Wei-Jie; He, Shiwei; Zou, Wen-Qing; Zhao, Yin; He, Qing‐Mei; Yang, Xiaojing; Guo, Rui; Mao, Yan‐Ping

doi:10.1177/15353702211037261

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…The expression of miR-BARTs 8-3p, 9-5p, 10-3p, and 22 was strongly correlated with an increase in tumor proliferation markers and signatures (Figure 8D). This is supported by experimental studies showing that each of these miRNAs induced proliferation in cell line models [80][81][82][83][84]. Importantly, this high proliferation index is somehow antagonistic to immune infiltration.…”

Section: Discussionmentioning

confidence: 68%

Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion

Salnikov

Prusinkiewicz

Lin

et al. 2023

Viruses

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Epstein–Barr virus (EBV) is a gamma-herpesvirus associated with 10% of all gastric cancers (GCs) and 1.5% of all human cancers. EBV-associated GCs (EBVaGCs) are pathologically and clinically distinct entities from EBV-negative GCs (EBVnGCs), with EBVaGCs exhibiting differential molecular pathology, treatment response, and patient prognosis. However, the tumor immune landscape of EBVaGC has not been well explored. In this study, a systemic and comprehensive analysis of gene expression and immune landscape features was performed for both EBVaGC and EBVnGC. EBVaGCs exhibited many aspects of a T cell-inflamed phenotype, with greater T and NK cell infiltration, increased expression of immune checkpoint markers (BTLA, CD96, CTLA4, LAG3, PD1, TIGIT, and TIM3), and multiple T cell effector molecules in comparison with EBVnGCs. EBVaGCs also displayed a higher expression of anti-tumor immunity factors (PDL1, CD155, CEACAM1, galectin-9, and IDO1). Six EBV-encoded miRNAs (miR-BARTs 8-3p, 9-5p, 10-3p, 22, 5-5p, and 14-3p) were strongly negatively correlated with the expression of immune checkpoint receptors and multiple markers of anti-tumor immunity. These profound differences in the tumor immune landscape between EBVaGCs and EBVnGCs may help explain some of the observed differences in pathological and clinical outcomes, with an EBV-positive status possibly being a potential biomarker for the application of immunotherapy in GC.

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Section: Discussionmentioning

confidence: 68%

Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion

Salnikov

Prusinkiewicz

Lin

et al. 2023

Viruses

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“…And Luo etc. demonstrated that differentiation is close to EBV, which indicates that it was a link between EBV and NPC [ 13 ]. Based on clinical consensus and the previous research, we kept the pathological in the model.…”

Section: Resultsmentioning

confidence: 99%

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China

Miao

Lei

et al. 2022

Cancer Cell Int

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Objective Nasopharyngeal carcinoma (NPC) is prevailing in Southern China, characterized by distinct geographical distribution. Aimed to predict the overall survival (OS) of patients with nasopharyngeal carcinoma, this study developed and validated nomograms considering demographic variables, hematological biomarkers, and oncogenic pathogens in China. Methods The clinicopathological and follow-up data of the nasopharyngeal carcinoma patients obtained from a prospective longitudinal cohort study in the Chongqing University Cancer Hospital between Jan 1, 2017 and Dec 31, 2019 ($$\mathrm{n}=1635$$ n = 1635 ). Cox regression model was used to tested the significance of all available variables as prognostic factors of OS. And independent prognostic factors were identified based on multivariable analysis to model nomogram. Concordance index (C-index), area under the receiver operating characteristic (AUC), calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of nomogram. Results Data was randomly divided into a training cohort (1227 observers, about 70% of data) and a validation group (408 observers, about 30% of data). At multivariable analysis, the following were independent predictors of OS in NPC patients and entered into the nomogram: age (hazard ratio [HR]: 1.03), stage (stage IV vs. stage I–II, HR: 4.54), radiotherapy (Yes vs. No, HR: 0.43), EBV ($$\ge 1000$$ ≥ 1000 vs.$$<1000$$ < 1000 , HR: 1.92), LAR ($$>6.15$$ > 6.15 vs.$$\le 6.15$$ ≤ 6.15 , HR: 2.05), NLR ($$>4.84$$ > 4.84 vs. $$\le 4.84$$ ≤ 4.84 HR: 1.54), and PLR ($$>206.33$$ > 206.33 vs.$$\le 206.33$$ ≤ 206.33 , HR: 1.79). The C-indexes for training cohort at 1-, 3- and 5-year were 0.73, 0.83, 0.80, respectively, in the validation cohort, the C-indexes were 0.74 (95% CI 0.63–0.86), 0.80 (95% CI 0.73–0.87), and 0.77 (95% CI 0.67–0.86), respectively. The calibration curve demonstrated that favorable agreement between the predictions of the nomograms and the actual observations in the training and validation cohorts. In addition, the decision curve analysis proved that the nomogram model had the highest overall net benefit. Conclusion A new prognostic model to predict OS of patients with NPC was developed. This can offer clinicians treatment making and patient counseling. Furthermore, the nomogram was deployed into a website server for use.

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A comprehensive overview on the crosstalk between microRNAs and viral pathogenesis and infection

Bahojb Mahdavi,

Jebelli,

Aghbash

et al. 2024

Medicinal Research Reviews

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Infections caused by viruses as the smallest infectious agents, pose a major threat to global public health. Viral infections utilize different host mechanisms to facilitate their own propagation and pathogenesis. MicroRNAs (miRNAs), as small noncoding RNA molecules, play important regulatory roles in different diseases, including viral infections. They can promote or inhibit viral infection and have a pro‐viral or antiviral role. Also, viral infections can modulate the expression of host miRNAs. Furthermore, viruses from different families evade the host immune response by producing their own miRNAs called viral miRNAs (v‐miRNAs). Understanding the replication cycle of viruses and their relation with host miRNAs and v‐miRNAs can help to find new treatments against viral infections. In this review, we aim to outline the structure, genome, and replication cycle of various viruses including hepatitis B, hepatitis C, influenza A virus, coronavirus, human immunodeficiency virus, human papillomavirus, herpes simplex virus, Epstein–Barr virus, Dengue virus, Zika virus, and Ebola virus. We also discuss the role of different host miRNAs and v‐miRNAs and their role in the pathogenesis of these viral infections.

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Epstein-Barr virus microRNA BART10-3p promotes dedifferentiation and proliferation of nasopharyngeal carcinoma by targeting ALK7

Cited by 6 publications

References 44 publications

Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion

Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China

A comprehensive overview on the crosstalk between microRNAs and viral pathogenesis and infection

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