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.

Oral manifestations of amyloidosis and the diagnostic applicability of oral tissue biopsy.

BACKGROUND: Amyloidosis exhibits a variable spectrum of systemic signs and oral manifestations that can be difficult to diagnose. This study aimed to characterize the clinical, demographic, and microscopic features of amyloidosis in the oral cavity. METHODS: This collaborative study involved three Brazilian oral pathology centers and described cases with a confirmed diagnosis of amyloidosis on available oral tissue biopsies. Clinical data were obtained from medical records. H&E, Congo-red, and immunohistochemically stained slides were analyzed. RESULTS: Twenty-six oral biopsies from 23 individuals (65.2% males; mean age: 59.6 years) were included. Oral involvement was the first sign of the disease in 67.0% of cases. Two patients had no clinical manifestation in the oral mucosa, although the histological analysis confirmed amyloid deposition. Amyloid deposits were distributed in perivascular (88.0%), periacinar and periductal (80.0%), perineurial (80.0%), endoneurial (33.3%), perimuscular (88.2%), intramuscular (94.1%), and subepithelial (35.3%) sites as well as around fat cells (100.0%). Mild/moderate inflammation was found in 65.4% of cases and 23.1% had giant cells. CONCLUSIONS: Amyloid deposits were consistently found in oral tissues, exhibiting distinct deposition patterns. Oral biopsy is less invasive than internal organ biopsy and enables the reliable identification of amyloid deposits even in the absence of oral manifestations. These findings corroborate the relevance of oral biopsy for the diagnosis of amyloidosis.

Burning mouth in oncology care: a systematic review.

Burning mouth, also referred to as oral dysesthesia, is an underreported condition among cancer patients that may represent an early symptom of cancer or an adverse effect of treatment. This review sought to characterize this symptom in oncology care where burning symptoms may occur. A systematic review of the literature was performed based on the PRISMA statement, and the protocol was registered at PROSPERO database. A structured search was done using eight databases. The process of study selection was conducted in two distinct phases. The JBI Critical Appraisal Tools were utilized to evaluate the risk of bias in the studies included. Of the total number of studies assessed, sixteen met the eligibility criteria. Of these studies included, 7 were case reports, 7 cross-sectional studies, and 2 non-randomized clinical trials. Most studies presented low risk of bias (n = 9), while the remaining studies were evaluated and scored as moderate (n = 5) or high (n = 2) risk of bias. Burning mouth was reported as a first symptom of cancer in three studies, and as an adverse event of radiotherapy (n = 2), chemoradiotherapy (n = 2), and chemotherapy (n = 9). Burning mouth was a first symptom in 0.62% of oral squamous cell carcinoma (OSCC), and 3.3% of patients with pain as chief complaint. Oral dysesthesia prevalence was 13.6% in patients experiencing chemotherapy-induced oral adverse events. The symptom of burning mouth should be examined in oncology care, as it may be underreported and therefore undertreated. New therapies may be related to a higher risk of oral burning and studies assessing approach to management are needed. Current management borrows from the current management of burning mouth in the non-cancer setting.

Oral cancer diagnosis communication: Impact of an educational intervention using the SPIKES protocol.

OBJECTIVE: This study evaluated the influence of a single educational intervention on the perception and knowledge of strategies for communicating oral cancer diagnoses. METHODS: A educational intervention, 72 dentists and 41 dental undergraduates participated in the 'Maio Vermelho Project', a continuing education activity. Participants completed a 14-question online questionnaire concerning their experiences and perceptions of delivering difficult news. The educational intervention featured an interview illustrating the SPIKES protocol, broadcast on YouTube. RESULTS: Participants had a mean age of 40 years. A minority (21.2%) had encountered or experienced communicating an oral cancer diagnosis. Exposure to lectures on this topic during their education was uncommon (22.1%) but more prevalent among students. After the intervention, confidence in communicating a cancer diagnosis (29.2%) and addressing the patient's family (30.1%) in line with the SPIKES protocol increased. CONCLUSION: A training deficit persists in delivering cancer diagnoses, highlighting the need for educational interventions to empower students and professionals in this critical procedure. Integration of this topic into the dental undergraduate curriculum is imperative. CLINICAL RELEVANCE: Effectively communicating a cancer diagnosis poses challenges to healthcare professionals, impacting treatment outcomes. Implementing educational interventions ensures that professionals are well prepared to navigate this complex task, ultimately improving patient care.

Histopathological risk assessment in multisite epithelial dysplasia: A meta-analysis.

OBJECTIVE: Histopathological grading of oral epithelial dysplasia (OED) is the current standard for stratifying cancer progression risk but is associated with subjectivity and variability. This problem is not commonly seen regarding the grading of epithelial dysplasia in other sites. This systematic review aims to compare grading systems for oral, anal, penile, and cervical epithelial dysplasia to determine their predictive accuracy for recurrence and malignant transformation (MT) outcomes. METHODS: The review protocol was registered in PROSPERO (CRD42023403035) and was reported according to the PRISMA checklist. A comprehensive search was performed in the main databases and gray literature. The risk of bias in individual studies was analyzed using the Joanna Briggs Institute checklist for each study design. RESULTS: Forty-six studies were deemed eligible and included in this systematic review, of which 45 were included in the quantitative analysis. Meta-analysis revealed that the binary system demonstrated a higher predictive ability for MT/recurrence of OED compared to multilevel systems. Higher predictive accuracy of MT was also observed for binary grading systems in anal intraepithelial neoplasia. CONCLUSIONS: No significant difference was found between the current grading systems of epithelial dysplasia in different body parts. However, binary grading systems have shown better clinical outcomes.

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.

ChatGPT and dentistry: a step toward the future.

This article aims to explore the integration of ChatGPT, an advanced conversational artificial intelligence model, in the field of dentistry. The review primarily consists of information related to the capabilities and functionalities of ChatGPT and how these abilities can aid dental professionals. This study includes data from research papers, case studies, and relevant literature on language models, as well as papers on dentistry, patient communication, dental education, and clinical decision-making. A systematic approach was used to select relevant studies and literature. The selection criteria focused on papers that specifically discussed the integration of language models, ChatGPT in particular, in dentistry and their applications. The study findings revealed that ChatGPT has significant potential to revolutionize dentistry by offering various applications and benefits. It can enhance patient engagement and understanding through personalized oral health information and guidance. In dental education, ChatGPT can provide interactive learning, case studies, and virtual patient simulations. ChatGPT can also assist researchers in analyzing dental literature, identifying patterns, and generating insights. Moreover, it supports dentists with evidence-based recommendations, treatment options, and diagnostic support. Integrating ChatGPT in dentistry can be highly beneficial, but it is crucial to address ethical considerations, accuracy, and privacy concerns. Responsible implementation and continuous improvement of its functionalities are necessary to ensure that patient care and outcomes are improved.

Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

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.

Association between autoimmune rheumatic diseases and head and neck cancer: Systematic review and meta-analysis.

BACKGROUND: Personal history of autoimmune rheumatic diseases has been implicated in the development of malignant neoplasms. Our aim was to assess the risk of head and neck (H&N) cancers in patients with autoimmune rheumatic diseases. METHODS: The articles search included PubMed, EMBASE, LILACS, The Cochrane Library, CINAHL, Scopus, Web of Science, and Google Scholar with no language restrictions for studies published from inception of the databases to August 20, 2022, assessing the risk of H&N cancer in patients with autoimmune rheumatic diseases. Studies were included if they reported the standardized incidence ratio (SIR) with corresponding 95% confidence intervals (CIs). The primary outcome was risk of H&N cancers in patients with autoimmune rheumatic diseases compared with the general population. Pooled summary estimates were calculated using a random-effects model, and subgroup analyses were done to establish whether risk of H&N cancers varied according to study site. RESULTS: Our search identified 5378 records, of which 32 cohort studies were eligible for systematic review and 24 for meta-analysis (including 273 613 patients). A significant association was found between H&N cancer and autoimmune rheumatic diseases (SIR = 2.35; 95% CI: 1.57-3.50; p < 0.01, I2  = 94%). CONCLUSION: Our study suggests that patients with autoimmune rheumatic diseases had a significantly increased risk of H&N cancer compared with the general population, including thyroid, oral, and nasopharyngeal cancers. These findings have implications for the individualized screening of these patients and the planning of oncology units. The protocol is registered with PROSPERO, number CRD42020197827.

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.

Symptomatic subgemmal neurogenous plaque in patients with COVID-19: Is there an association?

BACKGROUND: Subgemmal neurogenous plaques (SNP) are composed of neural structures found in the posterolateral portion of the tongue, rarely biopsied as most of them are asymptomatic or eventually only clinically managed. We aimed to investigate a case series of possible correlation of symptomatic subgemmal neurogenous plaque (SNP) with coronavirus disease 2019 (COVID-19). METHODS: Eleven formalin-fixed paraffin-embedded cases from patients with previous confirmed COVID-19 (by RT-PCR) were retrieved from two pathology files. Histological sections were morphologically studied, and then submitted to immunohistochemical reactions against S-100 and neurofilament proteins, neuron-specific enolase, Glial fibrillary acidic protein (GFAP), synaptophysin, CD56, Ki67, cytokeratins (7, 8-18, 19, 20), nucleocapsid and spike proteins (SARS-CoV-1; and -2) and epithelial membrane antigen (EMA) antibodies. Clinical data were retrieved from the patients' medical files, including the symptoms and the complete history of the progression of the disease. RESULTS: The patients who had COVID-19 included in this study experienced painful lesions in the tongue that corresponded to prominent or altered SNP. Microscopically, neural structures were positive for S-100, GFAP and neurofilament protein. And the cellular proliferative index (by Ki-67) was very low. CONCLUSION: Thus, based on the current results, we hypothesize that symptomatic SNP may be a late manifestation of COVID-19 infection.

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).

Quality assessment of PBM protocols for oral complications in head and neck cancer patients: part 2.

PURPOSE: To investigate the role of photobiomodulation (PBM) in patients undergoing head and neck cancer (HNC) treatment. We focused on the consequences of the main complications, such as quality of life (QoL), analgesia, functional impairment, and nutritional status, as well as on the impact on survival/ recurrences, radiotherapy (RT) interruption, adherence, cost-effectiveness, safety, feasibility, and tolerability. METHODS: An electronic search in PubMed and Scopus databases was performed. Full texts were carefully assessed, and data were assimilated into a tabular form for discussion and consensus among the expert panel. RESULTS: A total of 22 papers were included. Overall, a beneficial effect of PBM was evidenced in the amelioration of QoL, nutritional status, the reduction of pain, and functional impairment. Preventive PBM may reduce the incidence and duration of RT interruptions, potentially contributing to improved cancer treatment outcomes. PBM treatments are safe and recommended for routine use, with the caveat of avoiding direct tumor exposures where feasible. However, it does not appear to impact cancer survivorship/recurrences directly. Despite additional clinical efforts involving routine PBM use, the individual and public health benefits will positively impact oncology care. CONCLUSIONS: Quality of life, pain and functional impairment, nutritional status, and survival may be effectively improved with PBM. Given its established efficacy also in reducing RT interruptions and its safety, feasibility, and tolerability, PBM should be included in the field of supportive cancer care in HNC patients. Improved understanding of PBM mechanisms and precise dose parameters is enabling the generation of more robust, safe, and reproducible protocols; thus, it is imperative to support further clinical implementation as well as both applied and basic science research in this novel field.

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.

Impact of photobiomodulation in a patient-derived xenograft model of oral squamous cell carcinoma.

BACKGROUND: Photobiomodulation therapy (PBMT) is an effective method for the prevention of oral mucositis. However, the effects of PBMT on oral squamous cell carcinoma (OSCC) have not yet been fully elucidated. This study aimed to evaluate the impact of PBMT in an OSCC-patient-derived xenograft (OSCC-PDX) model. METHODS: BALB/c nude mice with OSCC-PDX models were divided into Control, without PBMT (n = 8); Immediate irradiation, PBMT since one week after tumor implantation (n = 6); and Late irradiation, PBMT after tumors reached 200 mm3 (n = 6). OSCC-PDX were daily irradiated (660 nm; 100 mW; 6 J/cm2 ; 0,2 J/point) for 12 weeks. The tumors were collected and submitted to volumetric, histological, immunohistochemistry, and cell cycle analysis. RESULTS: No significant differences in the volumetric measurements (p = 0.89) and in the histopathological grade (p > 0.05) were detected between the groups. The immunohistochemical analysis of Ki-67 (p = 0.9661); H3K9ac (p = 0.3794); and BMI1 (p = 0.5182), and the evaluation of the cell cycle phases (p > 0.05) by flow cytometry also did not demonstrate significant differences between the irradiated and non-irradiated groups. CONCLUSION: In this study, PBMT did not impact the behavior of OSCC-PDX models. This is an important preclinical outcome regarding safety concerns of the use of PBMT in cancer patients.