Analysis of Epstein-Barr Virus (EBV) and PD-L1 Expression in Nasopharyngeal Carcinoma Patients in a Non-Endemic Region.

BACKGROUND: The purpose of this study was to evaluate the status of Epstein-Barr virus (EBV) infection and the expression of programmed cell death ligand-1 (PD-L1) in tumor samples from patients with nasopharyngeal carcinoma (NPC). METHODS: Evaluation of EBV infection was performed through the detection of EBV-encoded small ribonucleic acids (EBER) by in situ hybridization, and PD-L1 expression was performed through immunohistochemistry. RESULTS: In total, 124 samples were evaluated for EBER and 120 for PD-L1 expression. A total of 86.3% of cases were positive for EBER and 55.8% were positive for PD-L1. There was a correlation between EBER positivity and the presence of undifferentiated carcinoma histology (p = 0.007) as well as the absence of tobacco history (p = 0.019). There was a correlation between PD-L1 expression and EBER positivity (p = 0.004). There was no statistically significant difference between overall survival (OS) and EBER (p = 0.290) or PD-L1 (p = 0.801) expression. CONCLUSIONS: This study corresponds to one of the largest cohorts of NPC in a non-endemic region. Phase III studies with checkpoint inhibitors are ongoing and may provide more data about the role of PD-L1 expression in this disease.

Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals.

The diagnosis of cancer and other diseases using data from non-specific sensors - such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms. The distinction between healthy individuals and patients with cancer on the floor of mouth or oral cavity could only be made with supervised learning. Accuracy above 80% was obtained for the binary classification (YES or NO for cancer) using a Support Vector Machine (SVM) with radial basis function kernel and Random Forest. In the classification considering the type of cancer, the accuracy dropped to ca. 70%. The accuracy tended to increase when clinical information such as alcohol consumption was used in conjunction with the e-tongue data. With the random forest algorithm, the rules to explain the diagnosis could be identified using the concept of Multidimensional Calibration Space. Since the training of the machine learning algorithms is believed to be more efficient when the data of a larger number of patients are employed, the approach presented here is promising for computer-assisted diagnosis.

Genosensor made with a self-assembled monolayer matrix to detect MGMT gene methylation in head and neck cancer cell lines.

DNA methylation is involved in the oncogenesis of head and neck squamous cell carcinoma and could be used for early detection of cancer to increase the chances of cure, but unfortunately diagnosis is usually made at late stages of the disease. In this work we developed genosensors to detect DNA methylation of the MGMT gene in head and neck cancer cell lines. The probe for MGMT promoter methylation was immobilized on gold electrodes modified with 11-mercaptoundecanoic acid (11-MUA) self-assembled monolayers (SAM). Detection was performed with electrochemical impedance spectroscopy, with clear distinction between methylated and non-methylated DNA from head and neck cell lines. The genosensor is sensitive with a low detection limit of 0.24 × 10-12 mol L-1. In addition, the cell lines FaDu, JHU28 and SCC25 for the MGMT gene, could be distinguished from the HN13 cell line which has a high degree of MGMT methylation (97%), thus confirming the selectivity. Samples with different percentages of MGMT DNA methylation could be separated in multidimensional projections using the visualization technique interactive document mapping (IDMAP). The genosensor matrix and the immobilization procedures are generic, and can be extended to other DNA methylation biomarkers.

Evaluation of DNA Methylation Changes and Micronuclei in Workers Exposed to a Construction Environment.

Methylation levels in tumor-suppressor genes and repetitive sequences have previously been used to study the relationship between environmental air pollution and epigenetic changes related to cancer. In this study, we measured the methylation profiles of the promoter regions CDKN2A, MLH1 and APC and the repetitive sequence LINE-1 in 59 workers exposed to the construction environment and in 49 unexposed workers. We also evaluated the micronuclei frequency and levels of trace elements in the blood of all workers. We evaluated of levels of particulate matter and polycyclic aromatic hydrocarbons (PAHs) at the construction site to characterize the environmental exposure. Our findings demonstrated that exposed workers exhibited significantly higher average levels of promoter methylation of CDKN2A, APC, and MLH1 genes and increased hypomethylation of the LINE-1 in comparison to unexposed workers (all p < 0.05). A higher frequency of micronuclei was observed in the exposed group (2 ± 2) compared to the unexposed group (1 ± 1) with p < 0.001. High levels of particulate matter (51⁻841 µg/m³) and some PAHs were found in samples from the construction environment. In summary, we provide evidence of increased DNA damage and altered DNA methylation of exposed workers, suggesting that genomic approaches to biomonitoring may be an effective way of estimating future cancer risk for construction workers.