Comprehensive Glycoprofiling of Oral Tumors Associates N-Glycosylation With Lymph Node Metastasis and Patient Survival.

While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), the heterogeneous and dynamic glycoproteome of tumor tissues from OSCC patients remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a cohort of resected primary tumor tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumor tissues displayed relatively uniform N-glycome profiles suggesting overall stable global N-glycosylation during disease progression, altered expression of six sialylated N-glycans was found to correlate with lymph node metastasis. Notably, glycoproteomics and advanced statistical analyses uncovered altered site-specific N-glycosylation revealing previously unknown associations with several clinicopathological features. Importantly, the glycomics and glycoproteomics data unveiled that comparatively high abundance of two core-fucosylated and sialylated N-glycans (Glycan 40a and Glycan 46a) and one N-glycopeptide from fibronectin were associated with low patient survival, while a relatively low abundance of N-glycopeptides from both afamin and CD59 were also associated with poor survival. This study provides insight into the complex OSCC tissue N-glycoproteome, thereby forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glycomarkers for OSCC.

Hematopoietic colony-stimulating factors in head and neck cancers: Recent advances and therapeutic challenges.

Colony-stimulating factors (CSFs) are key cytokines responsible for the production, maturation, and mobilization of the granulocytic and macrophage lineages from the bone marrow, which have been gaining attention for playing pro- and/or anti-tumorigenic roles in cancer. Head and neck cancers (HNCs) represent a group of heterogeneous neoplasms with high morbidity and mortality worldwide. Treatment for HNCs is still limited even with the advancements in cancer immunotherapy. Novel treatments for patients with recurrent and metastatic HNCs are urgently needed. This article provides an in-depth review of the role of hematopoietic cytokines such as granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), macrophage colony-stimulating factor (M-CSF), and interleukin-3 (IL-3; also known as multi-CSF) in the HNCs tumor microenvironment. We have reviewed current results from clinical trials using CSFs as adjuvant therapy to treat HNCs patients, and also clinical findings reported to date on the therapeutic application of CSFs toxicities arising from chemoradiotherapy.

The prognostic role of single cell invasion and nuclear diameter in early oral tongue squamous cell carcinoma.

BACKGROUND: The clinical significance of single cell invasion and large nuclear diameter is not well documented in early-stage oral tongue squamous cell carcinoma (OTSCC). METHODS: We used hematoxylin and eosin-stained sections to evaluate the presence of single cell invasion and large nuclei in a multicenter cohort of 311 cases treated for early-stage OTSCC. RESULTS: Single cell invasion was associated in multivariable analysis with poor disease-specific survival (DSS) with a hazard ratio (HR) of 2.089 (95% CI 1.224-3.566, P = 0.007), as well as with disease-free survival (DFS) with a HR of 1.666 (95% CI 1.080-2.571, P = 0.021). Furthermore, large nuclei were associated with worse DSS (HR 2.070, 95% CI 1.216-3.523, P = 0.007) and with DFS in multivariable analysis (HR 1.645, 95% CI 1.067-2.538, P = 0.024). CONCLUSION: Single cell invasion and large nuclei can be utilized for classifying early OTSCC into risk groups.

Feasibility study of ResNet-50 in the distinction of intraoral neural tumors using histopathological images.

BACKGROUND: Neural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for the histopathological classification of the three most prevalent benign neural tumor types: neurofibroma, perineurioma, and schwannoma. METHODS: A model was developed, trained, and evaluated for classification using the ResNet-50 architecture, with a database of 30 whole-slide images stained in hematoxylin and eosin (106, 782 patches were generated from and divided among the training, validation, and testing subsets, with strategies to avoid data leakage). RESULTS: The model achieved an accuracy of 70% (64% normalized), and showed satisfactory results for differentiating two of the three classes, reaching approximately 97% and 77% as true positives for neurofibroma and schwannoma classes, respectively, and only 7% for perineurioma class. The AUROC curves for neurofibroma and schwannoma classes was 0.83%, and 0.74% for perineurioma. However, the specificity rate for the perineurioma class was greater (83%) than in the other two classes (neurofibroma with 61%, and schwannoma with 60%). CONCLUSION: This investigation demonstrated significant potential for proficient performance with a limitation regarding the perineurioma class (the limited feature variability observed contributed to a lower performance).

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

BACKGROUND: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298). METHODS: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset. RESULTS: Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25). CONCLUSION: There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

Barriers to early diagnosis and management of oral cancer in Latin America and the Caribbean.

OBJECTIVE: This study aimed to explore perceived barriers to early diagnosis and management of oral cancer, as well as potential pathways for improvement in Latin America and the Caribbean (LAC). METHODS: This cross-sectional study used a self-administered online questionnaire created via the Research Electronic Data Capture platform. The survey was distributed to health professionals trained in Oral Medicine, Oral Pathology, Oral and Maxillofacial Surgery, and Dentists with clinical and academic expertise in oral potentially malignant disorder (OPMD) and oral cancer. Data obtained were systematically organized and analyzed descriptively using Microsoft Excel. RESULTS: Twenty-three professionals from 21 LAC countries participated. Major barriers included the limited implementation of OPMD and oral cancer control plans (17.4%), low compulsory reporting for OPMD (8.7%) and oral cancer (34.8%), unclear referral pathways for OPMD (34.8%) and oral cancer (43.5%), and a shortage of trained professionals (8.7%). Participants endorsed the utility of online education (100%) and telemedicine (91.3%). CONCLUSION: The survey highlights major perceived barriers to early diagnosis and management of OPMD and oral cancer in LAC, as well as potential avenues for improvement.

Changes in Body Mass Index Are Associated with Squamous Cell Carcinomas of Oral Cavity, Oropharynx and Larynx: A Case-Control Study in Brazil.

Head and neck cancer (HNC) significantly impacts nutritional status because the tumor limits swallowing function. In this sense, it is important to monitor the nutritional status throughout the life of any individual. A multicenter case-control study was carried out to analyze the BMI at 30 years of age, two years before diagnosis and at the time of diagnosis of individuals with oral cavity, oropharynx, and larynx cancers. It was observed that a 5% reduction in BMI during the two years before enrollment was associated with an increased risk of the oral cavity (OR = 3.73), oropharyngeal OR = 5.25), and laryngeal (OR = 5.22). Reduced BMI of more than 5% over two years before diagnosis was associated with HNC. Weight loss remained significant at diagnosis, but it is not possible to exclude reverse causality since most cases are at an advanced stage. BMI monitoring of individuals at potential risk for HNC can promote early diagnosis and nutritional interventions for HNC.

Diagnostic Accuracy of Liquid Biopsy for Oral Potentially Malignant Disorders and Head and Neck Cancer: an Overview of Systematic Reviews.

PURPOSE OF REVIEW: The aim of this overview is to appraise the evidence on salivary biomarkers for H&N cancer diagnosis. The acronym PICOS was used to develop the eligibility criteria and the focused review question: are liquid biopsies (saliva biomarkers) reliable for cancer detection in H&N cancer patients? Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, and LILACS. Risk of Bias (RoB) was assessed through AMSTAR 2. RECENT FINDINGS: A total of 20 SRs were included. Only seven SRs were able to reach more solid conclusions around the retrieved findings by calculating the pooled sensitivity, specificity, and the overall area under the curve (AUC). Despite the limitations, significant RoB, and lack of test metrics in primary studies, all SRs recognize and encourage the potential role of saliva in the early diagnosis of oral cancer.

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review.

Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.

INTRODUCTION: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. METHODS: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. CONCLUSION: The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.

FGFR1 is an important prognostic factor in oral leukoplakia and tongue squamous cell carcinoma.

BACKGROUND: Fibroblast growth factor receptor 1 is a potential prognostic factor for tongue squamous cell carcinoma and is associated with oral epithelial dysplasia grade in oral leukoplakia. METHODS: Thirty cases of tongue squamous cell carcinoma and 30 cases of oral leukoplakia were analyzed. Fibroblast growth factor receptor 1 and phosphorylated Akt protein expression were analyzed by immunohistochemistry and quantified using a digital algorithm. Fibroblast growth factor receptor 1 gene amplification was analyzed by fluorescent in situ hybridization in the tongue squamous cell carcinoma cases. RESULTS: Clinical appearance and dysplasia grade were correlated with oral leukoplakia malignant transformation. Oral leukoplakia cases presenting high fibroblast growth factor receptor 1 expression showed a higher risk of malignant transformation (p = 0.016, HR: 7.3, 95% CI: 1.4-37.4). Phosphorylated Akt showed faint to no expression in oral leukoplakia, which did not correlate with dysplasia grade or malignant transformation. High expression of fibroblast growth factor receptor 1 and phosohorylated Akt were associated with poor overall survival and disease-free survival in tongue squamous cell carcinoma, although only fibroblast growth factor receptor 1 expression was significantly associated with poor overall survival (p = 0.024; HR: 4.9, 95% CI: 1.2-19.9). Cases presenting double fibroblast growth factor receptor 1/phosphorylated Akt overexpression (n = 8) showed markedly impaired overall survival (p = 0.020; HR: 6.4, 95% CI: 1.3-31.1) and disease-free survival (p = 0.001, HR: 13.0, 95% CI: 3.0-55.7). Fibroblast growth factor receptor 1 amplification was observed in 16.6% of tongue squamous cell carcinoma cases, being correlated with vascular and neural invasion (p = 0.001 and 0.017, respectively), but not with fibroblast growth factor receptor 1 protein expression, overall survival, or disease-free survival. CONCLUSION: Fibroblast growth factor receptor 1 protein expression is an important prognostic factor in oral leukoplakia and tongue squamous cell carcinoma.

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

BACKGROUND: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility. METHODS: Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma). RESULTS: The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1-score, respectively. CONCLUSIONS: The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal.

BACKGROUND: Dysplasia grading systems for oral epithelial dysplasia are a source of disagreement among pathologists. Therefore, machine learning approaches are being developed to mitigate this issue. METHODS: This cross-sectional study included a cohort of 82 patients with oral potentially malignant disorders and correspondent 98 hematoxylin and eosin-stained whole slide images with biopsied-proven dysplasia. All whole-slide images were manually annotated based on the binary system for oral epithelial dysplasia. The annotated regions of interest were segmented and fragmented into small patches and non-randomly sampled into training/validation and test subsets. The training/validation data were color augmented, resulting in a total of 81,786 patches for training. The held-out independent test set enrolled a total of 4,486 patches. Seven state-of-the-art convolutional neural networks were trained, validated, and tested with the same dataset. RESULTS: The models presented a high learning rate, yet very low generalization potential. At the model development, VGG16 performed the best, but with massive overfitting. In the test set, VGG16 presented the best accuracy, sensitivity, specificity, and area under the curve (62%, 62%, 66%, and 65%, respectively), associated with the higher loss among all Convolutional Neural Networks (CNNs) tested. EfficientB0 has comparable metrics and the lowest loss among all convolutional neural networks, being a great candidate for further studies. CONCLUSION: The models were not able to generalize enough to be applied in real-life datasets due to an overlapping of features between the two classes (i.e., high risk and low risk of malignization).

Peptidomics-Driven Strategy Reveals Peptides and Predicted Proteases Associated With Oral Cancer Prognosis.

Protease activity has been associated with pathological processes that can lead to cancer development and progression. However, understanding the pathological unbalance in proteolysis is challenging because changes can occur simultaneously at protease, their inhibitor, and substrate levels. Here, we present a pipeline that combines peptidomics, proteomics, and peptidase predictions for studying proteolytic events in the saliva of 79 patients and their association with oral squamous cell carcinoma (OSCC) prognosis. Our findings revealed differences in the saliva peptidome of patients with (pN+) or without (pN0) lymph-node metastasis and delivered a panel of ten endogenous peptides correlated with poor prognostic factors plus five molecules able to classify pN0 and pN+ patients (area under the receiver operating characteristic curve > 0.85). In addition, endopeptidases and exopeptidases putatively implicated in the processing of differential peptides were investigated using cancer tissue gene expression data from public repositories, reinforcing their association with poorer survival rates and prognosis in oral cancer. The dynamics of the OSCC-related proteolysis were further explored via the proteomic profiling of saliva. This revealed that peptidase/endopeptidase inhibitors exhibited reduced levels in the saliva of pN+ patients, as confirmed by selected reaction monitoring-mass spectrometry, while minor changes were detected in the level of saliva proteases. Taken together, our results indicated that proteolytic activity is accentuated in the saliva of patients with OSCC and lymph-node metastasis and, at least in part, is modulated by reduced levels of salivary peptidase inhibitors. Therefore, this integrated pipeline provided better comprehension and discovery of molecular features with implications in the oral cancer metastasis prognosis.

Discovery proteomics reveals potential protein signature associated with malignant phenotype acquisition in pleomorphic adenoma.

OBJECTIVE: To analyze the proteomic profile of salivary pleomorphic adenoma (PA) and carcinoma ex pleomorphic adenoma (CXPA) samples and correlate them with the malignant transformation of the PA. MATERIALS AND METHODS: Thirty samples (10 PA, 16 CXPA, and 4 residual PA) were microdissected and submitted to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The proteomic data and protein identification were analyzed through LC-MS/MS spectra using the MaxQuant software. RESULTS: The proteomic analysis identified and quantified a total of 240 proteins in which 135 were found in PA, residual PA, and CXPA. The shared proteins were divided into six subgroups, and the proteins that showed statistically significant differences (p > 0.05) and fold-change > or <2.5 in one subgroup to another subgroup were included. Seven proteins (Apolipoprotein A-I-APOA1, haptoglobin-HP, protein of the synaptonemal complex 1-SYCP1, anion transport protein of band 3-SLC4A1, subunit µ1 of AP-1 complex-AP1M1, beta subunit of hemoglobin-HBB, and dermcidin-DCD) were classified as potential protein signatures, being HP, AP1M1, and HBB with higher abundance for PA to residual PA, APOA1 with higher abundance for PA to CXPA, SLC4A1 with lower abundance in the PA to CXPA, SYCP1with lower abundance for residual PA to CXPA, and DCD with higher abundance in the CXPA with epithelial differentiation to myoepithelial differentiation. CONCLUSIONS: In this work, we demonstrated the comparative proteomic profiling of PA, residual PA, and CXPA, and seven were proposed as protein signatures, some of which may be associated with the malignant phenotype acquisition.

FASN inhibition sensitizes metastatic OSCC cells to cisplatin and paclitaxel by downregulating cyclin B1.

OBJECTIVES: To investigate the potential effect of fatty acid synthase (FASN) inhibitor orlistat to enhance the effectiveness of chemotherapy drugs widely used to treat oral squamous cell carcinomas (OSCC), such as 5-fluorouracil, cisplatin, and paclitaxel. METHODS: The OSCC SCC-9 LN-1 metastatic cell line, which expresses high levels of FASN, was used for drug combination experiments. Cell viability was analyzed by crystal violet staining and automatic cell counting. Apoptosis and cell cycle were analyzed by flow cytometry with Annexin-V/7-AAD and propidium iodide staining, respectively. Cyclin B1, Cdc25C, Cdk1, FASN, and ERBB2 levels were assessed by Western blotting. Finally, cell scratch and transwell assays were performed to assess cell migration and invasion. RESULTS: Inhibition of FASN with orlistat sensitized SCC-9 LN-1 cells to the cytotoxic effects of paclitaxel and cisplatin, but not 5-fluorouracil, which was accompanied by a significant reduction in cyclin B1. The suppression of proliferation, migration, and invasion of SCC-9 LN-1 cells induced by orlistat plus cisplatin or paclitaxel was not superior to the effects of chemotherapy drugs alone. CONCLUSION: Our results suggest that orlistat enhances the chemosensitivity of SCC-9 LN-1 cells to cisplatin and paclitaxel by downregulating cyclin B1.

Tertiary lymphoid structures associate with improved survival in early oral tongue cancer.

BACKGROUND: The clinical significance of tertiary lymphoid structures (TLSs) is not well-documented in early oral tongue squamous cell carcinoma (OTSCC). METHODS: A total of 310 cases of early (cT1-2N0) OTSCC were included in this multicenter study. Assessment of TLSs was conducted on hematoxylin and eosin-stained sections. TLSs were assessed both in the central part of the tumor and at the invasive front area. RESULTS: The presence of TLSs associated with improved survival of early OTSCC as presented by Kaplan-Meier survival analyses for disease-specific survival (P = 0.01) and overall survival (P = 0.006). In multivariable analyses, which included conventional prognostic factors, the absence of TLSs associated with worse disease-specific survival with a hazard ratio (HR) of 1.96 (95% CI 1.09-3.54; P = 0.025) and poor overall survival (HR 1.66, 95% CI 1.11-2.48; P = 0.014). CONCLUSION: Histological evaluation of TLSs predicts survival in early OTSCC. TLSs showed superior prognostic power independent of routine WHO grading and TNM staging system.

EMT in salivary gland tumors: the expression of microRNAs miR-155 and miR-200c is associated with clinical-pathological parameters.

BACKGROUND: Epithelial to mesenchymal transition promotes cell adhesion loss, enabling invasion and metastasis. MicroRNAs are a class of small non-codifying RNAs that regulate gene expression. OBJECTIVES: The aim of this study was to evaluate the expression of microRNAs that could regulate the expression of EMT factors in salivary gland tumors (SGTs). METHODS AND RESULTS: The expression of microRNAs miR-9, miR-34a, miR-101, miR-138, miR-155, and miR-200c-described in the literature to target EMT factors-was evaluated by Real-time RT-PCR (qPCR) in pleomorphic adenoma (PA), mucoepidermoid carcinoma (MEC) and adenoid cystic carcinoma (ACC) samples. Bioinformatics tools were applied to identify miR targets and immunohistochemistry was used to examine the expression of the proteins E-cadherin, Twist, ZEB-1, ß-Catenin, and c-Kit. Comparing miR expression among SGT types, we observed increased expression of miR-9, and miR-138 in PAs, and increased miR-155 expression in MECs. Low-grade MECs exhibited increased miR-155 expression (p = 0.032). MECs that generated lymph node metastases had increased miR-200c levels (p = 0.018). MECs tended to have decreased expression of EMT-related proteins when compared to the other SGT types (c-Kit p < 0.001, Twist p = 0.014, and ZEB p = 0.012). Notably, increased c-Kit expression was associated with the presence of perineural infiltration in ACC (p = 0.050). CONCLUSIONS: This study provides evidence of alterations in the expression of EMT-factors regulating miRs, especially of miR-9, miR-138, miR-155, and miR-200c. No significant relationships were found between the expression of these miRs and proteins associated with EMT in SGTs.

Cephaeline is an inductor of histone H3 acetylation and inhibitor of mucoepidermoid carcinoma cancer stem cells.

AIM: To evaluate the potential use of Cephaeline as a therapeutic strategy to manage mucoepidermoid carcinomas (MEC) of the salivary glands. MATERIAL AND METHODS: UM-HMC-1, UM-HMC-2, and UM-HMC-3A MEC cell lines were used to establish the effects of Cephaeline over tumor viability determined by MTT assay. In vitro wound healing scratch assays were performed to address cellular migration while immunofluorescence staining for histone H3 lysine 9 (H3k9ac) was used to identify the acetylation status of tumor cells upon Cephaeline administration. The presence of cancer stem cells was evaluated by the identification of ALDH enzymatic activity by flow cytometry and through functional assays using in vitro tumorsphere formation. RESULTS: A single administration of Cephaeline resulted in reduced viability of MEC cells along with the halt on tumor growth and cellular migration potential. Administration of Cephaeline resulted in chromatin histone acetylation as judged by the increased levels of H3K9ac and disruption of tumorspheres formation. Interestingly, ALDH levels were increased in UM-HMC-1 and UM-HMC-3A cell lines, while UM-HMC-2 showed a reduced enzymatic activity. CONCLUSION: Cephaeline has shown anti-cancer properties in all MEC cell lines tested by regulating tumor cells' viability, migration, proliferation, and disrupting the ability of cancer cells to generate tumorspheres.

The hidden curve behind COVID-19 outbreak: the impact of delay in treatment initiation in cancer patients and how to mitigate the additional risk of dying-the head and neck cancer model.

PURPOSE: The rapid spread of the SARS-CoV-2 pandemic around the world caused most healthcare services to turn substantial attention to treatment of these patients and also to alter the structure of healthcare systems to address an infectious disease. As a result, many cancer patients had their treatment deferred during the pandemic, increasing the time-to-treatment initiation, the number of untreated patients (which will alter the dynamics of healthcare delivery in the post-pandemic era) and increasing their risk of death. Hence, we analyzed the impact on global cancer mortality considering the decline in oncology care during the COVID-19 outbreak using head and neck cancer, a known time-dependent disease, as a model. METHODS: An online practical tool capable of predicting the risk of cancer patients dying due to the COVID-19 outbreak and also useful for mitigation strategies after the peak of the pandemic has been developed, based on a mathematical model. The scenarios were estimated by information of 15 oncological services worldwide, given a perspective from the five continents and also some simulations were conducted at world demographic data. RESULTS: The model demonstrates that the more that cancer care was maintained during the outbreak and also the more it is increased during the mitigation period, the shorter will be the recovery, lessening the additional risk of dying due to time-to-treatment initiation. CONCLUSIONS: This impact of COVID-19 pandemic on cancer patients is inevitable, but it is possible to minimize it with an effort measured by the proposed model.