Epstein-Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review

Identification and validation of a prognostic risk model based on radiosensitivity-related genes in nasopharyngeal carcinoma

BACKGROUND

Despite advancements with intensity-modulated radiation therapy (IMRT), about 10 % of nasopharyngeal carcinoma (NPC) patients remain resistant to radiotherapy, leading to recurrence and poor prognosis. This study aims to identify radiosensitivity-related genes in NPC and develop a prognostic model to predict patient outcomes.

METHODS

We analyzed 179 NPC samples from Fujian Cancer Hospital using RNA sequencing. Differentially expressed genes (DEGs) were identified between radiotherapy-sensitive and resistant samples. Machine learning algorithms and Cox regression were used to construct a prognostic risk model, validated in the GSE102349 dataset. Additional analyses included functional pathway, immune infiltration, and drug sensitivity.

RESULTS

A risk model based on six genes (LCN8, IGSF1, RIMS2, RBP4, TBX10, ETV4) was developed. Kaplan-Meier analysis showed significantly shorter progression-free survival (PFS) in the high-risk group. The model's AUC values were 0.872, 0.807, and 0.802 for 1-year, 3-year, and 5-year predictions. A nomogram including clinical factors was created, and enrichment analysis linked the high-risk group to radiotherapy resistance mechanisms.

CONCLUSIONS

This study established a novel radiosensitivity-related prognostic model, offering insights into NPC prognosis and radiotherapy resistance mechanisms.

Using machine learning algorithms to predict the prognosis of advanced nasopharyngeal carcinoma after intensity-modulated radiotherapy

BACKGROUND

The prognosis of advanced nasopharyngeal carcinoma (NPC) patients after intensity-modulated radiotherapy (IMRT) has not been well studied. We aimed to construct prognostic models for advanced NPC patients with stage III-IV after their first treatment with IMRT by using machine learning algorithms and to identify the most important predictors.

METHODS

A total of 427 patients treated in Meizhou People's Hospital in Guangdong province, China from January 1, 2013 to December 12, 2018 were enrolled in this study, with an average follow-up period of 7.16 years from July 2020 to March 2021. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. Three machine learning algorithms were applied to construct advanced NPC prognostic models: logistic regression (LR), decision tree (DT), and random forest (RF). Area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis were identified and ranked.

RESULTS

There were 50 (11.7%) NPC-related deaths observed in this study. The mean age of all participants was 49.39±11.29 years, of whom 299 (70.0%) were males. In general, RF showed the best predictive performance with the highest AUC (0.753, 95% CI: 0.609, 0.896), compared to LR (0.736, 95% confidence interval (CI): 0.590, 0.881), and DT (0.720, 95% CI: 0.520, 0.921). The six most important predictors identified by RF were Epstein-Barr virus deoxyribonucleic acid, aspartate aminotransferase, body mass index, age, blood glucose level, and alanine aminotransferase.

CONCLUSIONS

We proposed RF as a simple and accurate tool for the evaluation of the prognosis of advanced NPC patients after the treatment with IMRT in clinical settings.

γδ T cells: Major advances in basic and clinical research in tumor immunotherapy

ABSTRACT

γδ T cells are a kind of innate immune T cell. They have not attracted sufficient attention because they account for only a small proportion of all immune cells, and many basic factors related to these cells remain unclear. However, in recent years, with the rapid development of tumor immunotherapy, γδ T cells have attracted increasing attention because of their ability to exert cytotoxic effects on most tumor cells without major histocompatibility complex (MHC) restriction. An increasing number of basic studies have focused on the development, antigen recognition, activation, and antitumor immune response of γδ T cells. Additionally, γδ T cell-based immunotherapeutic strategies are being developed, and the number of clinical trials investigating such strategies is increasing. This review mainly summarizes the progress of basic research and the clinical application of γδ T cells in tumor immunotherapy to provide a theoretical basis for further the development of γδ T cell-based strategies in the future.

Down-regulated lncRNA ROR in tumor-educated platelets as a liquid-biopsy biomarker for nasopharyngeal carcinoma

PURPOSES

To evaluate the diagnostic value of tumor-educated platelets (TEP) lncRNA ROR for nasopharyngeal carcinoma (NPC).

METHODS

Quantitative real-time PCR was used to determine the expression level of TEP lncRNA ROR in NPC patients (n = 50) as compared to normal subjects (n = 33). The ROC curve analysis was performed to assess the diagnostic value of TEP lncRNA ROR for NPC. Correlations between TEP lncRNA ROR and clinical parameters were further analyzed.

RESULTS

The median of TEP lncRNA ROR was significantly lower in NPC patients than that in normal subjects (0.0209 vs 0.0610, p = 0.0019), while no significant difference was found in plasma lncRNA ROR. ROC analysis showed that TEP lncRNA ROR had a sensitivity of 60%, specificity of 70%, and accuracy of 63.9% in diagnosing NPC, and the area under ROC curve (AUC) was 0.70. The expression level of TEP lncRNA ROR in NPC showed no significant difference among different TNM stages. However, low level of TEP lncRNA ROR correlated well with positive Epstein-Barr virus (EBV) DNA (kappa value = 0.314, p = 0.06), TEP lncRNA ROR and EBV DNA had similar diagnostic positive rate (58.3%) for NPC, and the combination of TEP lncRNA ROR and EBV DNA increased the positive rate to 74%.

CONCLUSION

The expression level of TEP lncRNA ROR was down-regulated in NPC and the diagnostic value of TEP lncRNA ROR was similar to EBV DNA. Our study indicated that TEP lncRNA ROR might serve as a novel type of liquid biopsy biomarker in diagnosis of NPC patients.

Is EBV Associated with Breast Cancer in Specific Geographic Locations?

Epstein-Barr virus (EBV) is a virus that establishes a life-long infection in people, and infection with EBV is nearly ubiquitous by adulthood. EBV was identified from biopsy material from a child with Burkitt's lymphoma (BL) in sub-Saharan Africa. EBV has a well-characterised role in the development of some cancers, notably, Burkitt's lymphoma (BL), Hodgkin's disease (HD), gastric carcinoma (GC), and nasopharyngeal carcinoma (NPC). Links have also been made between EBV and breast cancer (BC), but these have been controversial. For all EBV-associated cancers, the ubiquitous nature of infection with EBV, contrasted with the relatively rare development of cancer, highlights a problem of determining whether EBV is an aetiological agent of cancer. In addition, the geographic distributions of some EBV-associated cancers point to contributions from additional co-factors. Recent meta-analyses of the incidence of EBV within BC biopsies has revealed that the diversity in the conclusions remain, however, they also show more of an association between EBV and BC biopsies in some study locations. Here, we review the evidence linking EBV with BC, and conclude that the evidence for the presence of EBV in BC biopsies is concentrated in specific geographic regions but is currently insufficient to provide a causal link. We pose some questions that could help to resolve the question of whether EBV contributes to BC and probe the contribution EBV might make to the aetiology of BC.