Correction: Epstein-Barr virus infection and oral squamous cell carcinoma risk: A meta-analysis

Identification of gene profiles related to the development of oral cancer using a deep learning technique

BACKGROUND

Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning.

METHODS

Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes.

RESULTS

Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes.

CONCLUSIONS

Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Role of the microbiome in oral cancer occurrence, progression and therapy

The oral cavity, like other digestive or mucosal sites, contains a site-specific microbiome that plays a significant role in maintaining health and homeostasis. Strictly speaking, the gastrointestinal tract starts from the oral cavity, with special attention paid to the specific flora of the oral cavity. In healthy people, the microbiome of the oral microenvironment is governed by beneficial bacteria, that benefit the host by symbiosis. When a microecological imbalance occurs, changes in immune and metabolic signals affect the characteristics of cancer, as well as chronic inflammation, disruption of the epithelial barrier, changes in cell proliferation and cell apoptosis, genomic instability, angiogenesis, and epithelial barrier destruction and metabolic regulation. These pathophysiological changes could result in oral cancer. Rising evidence suggests that oral dysbacteriosis and particular microbes may play a positive role in the evolution, development, progression, and metastasis of oral cancer, for instance, oral squamous cell carcinoma (OSCC) through direct or indirect action.

Prevalence of malignant neoplastic oral lesions among children and adolescents: A systematic review and meta-analysis

BACKGROUND

Malignant neoplasms that affect children and adolescents are predominantly embryonic and generally affect blood system cells and supporting tissues.

AIM

This study aimed to summarize the scientific evidence about the prevalence of malignant lesions in the oral cavity of children and adolescents.

DESIGN

In this systematic review and meta-analysis (PROSPERO CRD42020158338), data were obtained from seven databases and the gray literature. Cross-sectional observational studies on the prevalence of biopsied oral pediatric malignancies were included. The Newcastle-Ottawa Scale assessed the quality of the included studies, and the GRADE approach evaluated the evidence certainty. The meta-analysis prevalence was calculated using MedCalc® software, adopting a 95% confidence level (CI; random-effect model).

RESULTS

Forty-two studies were included in the meta-analysis. Of the 64,522 biopsies, the prevalence of malignant lesions was 1.93% (n = 1,100; 95% CI = 1.21%-2.80%). Countries with a low socioeconomic profile showed the highest prevalence. The sample size did not influence the prevalence of oral malignancies, and unspecified lymphomas (12.08%; 95% CI = 5.73%-20.37%) and rhabdomyosarcoma (10.53%; 95% CI = 7.28%-14.30%) were the most common lesions.

CONCLUSIONS

Oral malignant lesions biopsied in children and adolescents had a prevalence of <3%, and lymphomas and sarcomas were the most prevalent lesions.

Epstein-Barr virus may contribute to the pathogenesis of adult-onset recurrent respiratory papillomatosis: A preliminary study

OBJECTIVE

Human papillomavirus (HPV) causes adult-onset recurrent respiratory papillomatosis (AORRP), but AORPP prevalence is much lower than HPV prevalence. Thus, HPV infection is necessary, but not sufficient, to cause AORRP and other factors likely contribute to its pathogenesis. The present study aimed to investigate whether co-infection with herpetic viruses may contribute to the pathogenesis of AORRP.

DESIGN

Prospective case-control study conducted from January 2018 to November 2019.

SETTINGS

Tertiary referral centre.

PARTICIPANTS

Eighteen consecutive patients with AORRP and 18 adults with healthy laryngeal mucosa (control group) undergoing surgery.

MAIN OUTCOME MEASURES

Cytomegalovirus, Epstein-Barr virus (EBV), herpes simplex viruses 1 and 2, human herpesvirus 6, varicella zoster virus and HPV (including genotyping) were detected in biopsies of papilloma or healthy mucosa using real-time polymerase chain reaction and reverse line blot. Dysplasia and Ki67 levels were determined in papilloma specimens.

RESULTS

EBV was present in 6 (33.3%) AORRP patients and no control patients (P = .019). Presence was not dependent on tobacco exposure (P = .413) or HPV genotype or concentration (P > .999). EBV presence was strongly related to increased cell proliferation (P = .005) and number of previous surgeries (P = .039), but not dysplasia (P > .999). Human herpesvirus 6 was found in 3 (16.7%) AORRP biopsies, with one false positive. No other herpetic virus was found.

CONCLUSIONS

Unlike other herpetic viruses, EBV seems to interact with HPV, enhancing cell proliferation and contributing to the pathogenesis and progression of AORRP. Further research is required to elucidate specific interactions and their role in the pathogenesis of AORRP.