Detection Of Oral Squamous Cell Carcinoma Research Articles

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data.

Oral cancer, representing 2-4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein-Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.

Precision and speed: The AI revolution in oral squamous cell carcinoma detection

Oral Squamous Cell Carcinoma (OSCC) is a highly aggressive tumor with a poor prognosis and is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. AI can precisely analyze a vast dataset of various imaging modalities, such as fluorescent, hyperspectral, cytological, histological, radiological, endoscopic, clinical, and infrared thermal modalities. In this review, we discuss digital histopathological image analysis, computer vision and radiological analysis for the early detection of OSCC.

Advanced Techniques In Oral Oncology: AComprehensiveReview

The most common type of oral cancer, oral squamous cell carcinoma (OSCC), is still amajor global health concern. Due to delayed diagnoses, which are frequently caused by low public awareness and fewscreeningalternatives, the five-year survival rate is still low. Modernmethods in oral oncology have arisen to enhance patient outcomes and early identification, including as molecular and imaging diagnostics. This review showcases several cutting-edge diagnostic techniques, including flow cytometry, next-generation sequencing(NGS), chemiluminescence, toluidine blue, immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), and nano-diagnostics. By providing insights into tumor behaviourand enabling customized treatment approaches, these techniques advance our knowledge of the genetic and proteomic landscape of oral cancer. Combining these cutting-edge approaches with conventional diagnosticsmight greatly improve the accuracy of OSCC detection and risk assessment, despite drawbacks like false positives and expensive procedures. Improving patient care greatly depends on the creation of quick, noninvasive procedures and the investigation of genetic biomarkers. The present review highlightsthe need for continued research and training in oral oncology to maximize early diagnosis and intervention strategies

Enhancing Oral Squamous Cell Carcinoma Detection Using Histopathological Images: A Deep Feature Fusion and Improved Haris Hawks Optimization-Based Framework.

Oral cancer, also known as oral squamous cell carcinoma (OSCC), is one of the most prevalent types of cancer and caused 177,757 deaths worldwide in 2020, as reported by the World Health Organization. Early detection and identification of OSCC are highly correlated with survival rates. Therefore, this study presents an automatic image-processing-based machine learning approach for OSCC detection. Histopathological images were used to compute deep features using various pretrained models. Based on the classification performance, the best features (ResNet-101 and EfficientNet-b0) were merged using the canonical correlation feature fusion approach, resulting in an enhanced classification performance. Additionally, the binary-improved Haris Hawks optimization (b-IHHO) algorithm was used to eliminate redundant features and further enhance the classification performance, leading to a high classification rate of 97.78% for OSCC. The b-IHHO trained the k-nearest neighbors model with an average feature vector size of only 899. A comparison with other wrapper-based feature selection approaches showed that the b-IHHO results were statistically more stable, reliable, and significant (p < 0.01). Moreover, comparisons with those other state-of-the-art (SOTA) approaches indicated that the b-IHHO model offered better results, suggesting that the proposed framework may be applicable in clinical settings to aid doctors in OSCC detection.

Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies. Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.

Gene expression analysis of microRNA-1285 in the South Indian oral squamous cell carcinoma population.

Oral squamous cell carcinoma (OSCC) is the most common oral malignant tumor, which has poor prognosis. The traditional investigative modality is invasive biopsy which is the gold standard for diagnosis. In recent years, alternative methods like non-invasive biomarkers have been studied for their potential role in early diagnosis and prognosis. Among them, microRNAs (miRNAs or miRs) are short non-coding RNAs that regulate gene expression in various diseases, including OSCC. Several miRNAs are being researched as non-invasive biomarkers as well as novel therapeutic targets in the treatment of OSCC. MiR expression can be upregulated or downregulated in OSCC. Among the reported miRNAs, miR-1285 is an important miRNA found to be involved in OSCC. The aim of the current study was to quantify the levels of miR-1285 in OSCC samples and to validate their potential role as biomarkers for OSCC detection. Sixteen samples of cancer tissue and normal tissue were evaluated from a total of 25 patients, in the study, conducted in the Department of Oral and Maxillofacial Surgery. The tissues were processed for H&E staining and gene expression analysis of miR-1285. The samples were collected after proper informed consent from the patients. Total RNA isolated was reverse transcribed into cDNA which was used in the gene expression analysis using qRT-PCR. The histopathological examination confirmed the OSCC cases and the gene expression analysis revealed that miR-1285 was significantly downregulated in OSCC tissues. Since miR-1285 showed significant difference between the OSCC and normal tissues it could be postulated as a potential biomarker and therapeutic target for OSCC. Further in-vitro and in-vivo studies could validate their functional role in OSCC.

The Significance of Clinical Examination in Diagnosing Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor that arises from the squamous epithelium in the oral cavity. Timely identification of this condition is essential for effective treatment and improved patient outcomes. In this regard, clinical examination plays a pivotal role in the accurate detection and diagnosis of oral lesions, particularly OSCC located on the lateral border of the tongue. This case report aims to underscore the significance of a thorough clinical examination as an effective method for screening and diagnosing OSCC on the lateral border of the tongue. A precise and systematic clinical examination is crucial for the early detection of OSCC on the lateral border of the tongue, achieved through diligent visual assessment and careful palpation of suspicious areas. Emphasizing this approach by dental professionals enhances early diagnosis rates, leading to improved clinical outcomes and prognosis for patients affected by this severe condition.

Multispectral fluorescence imaging of EGFR and PD-L1 for precision detection of oral squamous cell carcinoma: a preclinical and clinical study

BackgroundEarly detection and treatment are effective methods for the management of oral squamous cell carcinoma (OSCC), which can be facilitated by the detection of tumor-specific OSCC biomarkers. The epidermal growth factor receptor (EGFR) and programmed death-ligand 1 (PD-L1) are important therapeutic targets for OSCC. Multispectral fluorescence molecular imaging (FMI) can facilitate the detection of tumor multitarget expression with high sensitivity and safety. Hence, we developed Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes, in combination with multispectral FMI, to sensitively and noninvasively identify EGFR and PD-L1 expression for the detection and comprehensive treatment of OSCC.MethodsThe expression of EGFR and PD-L1 was analyzed using bioinformatics data sources and specimens. Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes were developed and tested on preclinical OSCC cell line and orthotopic OSCC mouse model, fresh OSCC patients’ biopsied samples, and further clinical mouthwash trials were conducted in OSCC patients.ResultsEGFR and PD-L1 were specifically expressed in human OSCC cell lines and tumor xenografts. Nimotuzumab-ICG and Atezolizumab-Cy5.5 imaging probes can specifically target to the tumor sites in an in situ human OSCC mouse model with good safety. The detection sensitivity and specificity of Nimotuzumab-ICG in patients were 96.4% and 100%, and 95.2% and 88.9% for Atezolizumab-Cy5.5.ConclusionsEGFR and PD-L1 are highly expressed in OSCC, the combination of which is important for a precise prognosis of OSCC. EGFR and PD-L1 expression can be sensitively detected using the newly synthesized multispectral fluorescence imaging probes Nimotuzumab-ICG and Atezolizumab-Cy5.5, which can facilitate the sensitive and specific detection of OSCC and improve treatment outcomes.Trial registrationChinese Clinical Trial Registry, ChiCTR2100045738. Registered 23 April 2021, https://www.chictr.org.cn/bin/project/edit?pid=125220

Saliva-Based Biomarkers in Oral Squamous Cell Carcinoma Using OMICS Technologies: A Systematic Review

(1) Background: Head and neck cancer (HNC) is a major public health challenge worldwide, with oral squamous cell carcinoma (OSCC) being the predominant form. Despite advances in treatment, OSCC remains a major cause of morbidity and mortality due to delayed diagnosis and limited therapeutic efficacy. This study reviews omics technologies to assess new salivary biomarkers for the early detection of OSCC. (2) Methods: A comprehensive literature search in the last 20 years identified four relevant studies focusing on salivary biomarkers in OSCC. (3) Results: Proteomic and genomic analyses revealed significant changes in salivary composition between OSCC patients and healthy controls, suggesting promising diagnostic and prognostic biomarkers. However, studies showed varying degrees of bias, indicating the need for further research and improved standardization. (4) Conclusions: Saliva, with its advantages of ease of collection, minimal invasiveness, and potential for large-scale screening, is an emerging promising substrate for non-invasive biomarker research. Nonetheless, there is a need for improved biomarker sensitivity and specificity; currently, histological examination remains the golden standard.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection

ObjectivesDiagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.Methods4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented.ResultsThe detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938.ConclusionsOSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective.Clinical relevanceOral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.

Theranostic Potential of Bacteriophages against Oral Squamous Cell Carcinoma.

Oral Squamous Cell Carcinoma (OSCC) is a widespread and challenging disease that accounts for 94% of cancers of the oral cavity worldwide. Bacteriophages (phages) have shown promise as a potential theranostic agent for the treatment of OSCC. It may offer advantages in overcoming the challenges of conventional methods. Modern high-throughput pyrosequencing techniques confirm the presence of specific bacterial strains associated with OSCC. Bio-panning and filamentous phages facilitate visualization of the peptide on surfaces and show high affinity in OSCC cells. The peptide has the potential to bind integrin (αvβ6), aid in diagnosis, and inhibit the proliferation of OSCC cells. Mimotopes of tumor-associated antigens show cytotoxic and immune responses against cancer cells. Biomarker-based approaches such as transferrin enable early OSCC diagnosis. A modified temperate phage introduces CRISPR-Cas3 to target antimicrobial-resistant bacteria associated with OSCC. The research findings highlight the evolving field of phage diagnostics and therapy and represent a new avenue for non-invasive, targeted approaches to the detection and treatment of OSCC. However, extensive clinical research is required to validate the efficacy of phages in innovative cancer theranostic strategies.

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture

ProblemOral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment.AimThis study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer.MethodsA deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture.ResultsThe improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field.ConclusionIntegrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Oxidative Stress in the Pathogenesis of Oral Cancer.

Oxidative stress, arising from an imbalance between reactive oxygen species (ROS) and antioxidants, contributes significantly to oral cancer such as oral squamous cell carcinoma (OSCC) initiation, promotion, and progression. ROS, generated both internally and externally, induce cellular damage including DNA mutations and lipid peroxidation, fostering oncogene activation and carcinogenesis. The objective of this review was to cover and analyze the interplay between ROS and antioxidants, influencing the key processes such as cell proliferation, apoptosis, and angiogenesis, shaping the trajectory of OSCC development. Despite the promise of antioxidants to halt cancer progression and mitigate oxidative damage, their therapeutic efficacy remains debated. The conducted literature search highlighted potential biomarkers that indicate levels of oxidative stress, showing promise for the early detection and monitoring of OSCC. Furthermore, melatonin has emerged as a promising adjunct therapy for OSCC, exerting antioxidant and oncostatic effects by modulating tumor-associated neutrophils and inhibiting cancer cell survival and migration. In addition, this review aims to shed light on developing personalized therapeutic strategies for patients with OSCC such as melatonin therapy, which will be discussed. Research is needed to elucidate the underlying mechanisms and clinical implications of oxidative stress modulation in the context of oral cancer.

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Oral squamous cell carcinoma (OSCC) is a malignancy that destroys the ability of the tissues surrounding the mouth to develop layers and membranes. Automated early diagnosis of oral histopathological images has allowed for the successful diagnosis of oral cancer, thanks to recent advancements in Deep Learning (DL) for biomedical image classification. By using a convolutional neural network (CNN) model based on deep learning for the initial analysis of oral squamous cell carcinoma (OSCC), this work aims to automate the classification of benign and malignant oral biopsy histopathological images. For this study, the CNN model Inception-Resnet-V2 is selected using the transfer learning approach. To enhance OSCC detection, additional layers are incorporated into this pretrained model. By mining a repository of oral cancer histopathology images, we can gauge how well these tweaked models perform. We examine the modified structure of the pre-trained Inception-Resnet-V2 model and suggest a DL-CNN model that uses it. With an accuracy of 91.78%, it has outperformed in terms of performance metrics.

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Oral squamous cell carcinoma (OSCC) is a malignancy that destroys the ability of the tissues surrounding the mouth to develop layers and membranes. Automated early diagnosis of oral histopathological images has allowed for the successful diagnosis of oral cancer, thanks to recent advancements in Deep Learning (DL) for biomedical image classification. By using a convolutional neural network (CNN) model based on deep learning for the initial analysis of oral squamous cell carcinoma (OSCC), this work aims to automate the classification of benign and malignant oral biopsy histopathological images. For this study, the CNN model Inception-Resnet-V2 is selected using the transfer learning approach. To enhance OSCC detection, additional layers are incorporated into this pretrained model. By mining a repository of oral cancer histopathology images, we can gauge how well these tweaked models perform. We examine the modified structure of the pre-trained Inception-Resnet-V2 model and suggest a DL-CNN model that uses it. With an accuracy of 91.78%, it has outperformed in terms of performance metrics.

Low expression of interleukin-1 receptor antagonist correlates with poor prognosis via promoting proliferation and migration and inhibiting apoptosis in oral squamous cell carcinoma

BackgroundBiomarkers are biochemical indicators that can identify changes in the structure or function of systems, organs, or cells and can be used to monitor a wide range of biological processes, including cancer. Interleukin-1 receptor antagonist (IL1RA) is an important inflammatory suppressor gene and tumor biomarker. The goal of this study was to investigate the expression of IL1RA, its probable carcinogenic activity, and its diagnostic targets in oral squamous cell carcinoma (OSCC). ResultsWe discovered that IL1RA was expressed at a low level in OSCC tumor tissues compared to normal epithelial tissues and that the expression declined gradually from epithelial hyperplasia through dysplasia to carcinoma in situ and invasive OSCC. Low IL1RA expression was associated not only with poor survival but also with various clinicopathological markers such as increased infiltration, recurrence, and fatalities. Following cellular phenotyping investigations in OSCC cells overexpressing IL1RA, we discovered that recovering IL1RA expression decreased OSCC cell proliferation, migration, and increased apoptosis. ConclusionsIn summary, our investigation highlighted the possible involvement of low-expression IL1RA in OSCC cells in promoting invasive as well as metastatic and inhibiting apoptosis, as well as the efficacy of IL1RA-focused monitoring in the early detection and treatment of OSCC.

Deciphering the role of TLR3 polymorphisms in oral squamous cell carcinoma pathogenesis: A case-control study.

Oral squamous cell carcinoma (OSCC) poses a significant global health burden, particularly prevalent in regions like India. Despite advancements in diagnostics, early detection of OSCC remains challenging, necessitating novel diagnostic modalities. Toll-like receptors (TLRs) and their polymorphisms have emerged as potential contributors to OSCC pathogenesis. This retrospective case-control study examined 120 individuals, including 60 OSCC cases and 60 healthy controls. Genotyping of TLR3 single-nucleotide polymorphisms (SNPs) rs3775290 and rs3775291 was conducted using TaqMan allelic discrimination real-time polymerase chain reaction. Functional consequence analysis and TLR3 expression profiling were performed to elucidate their role in OSCC pathogenesis. Significant associations were observed between TLR3 SNPs and OSCC susceptibility, particularly at loci rs3775290 and rs3775291. Functional consequence analysis revealed pathogenic mutations in TLR3 genes, potentially affecting protein structure and function. TLR3 overexpression was detected in OSCC lesions, implicating its involvement in disease progression. TLR3 polymorphisms play a pivotal role in OSCC pathogenesis, offering potential biomarkers for diagnosis and prognosis. Targeting TLR3-mediated pathways may hold promise in personalised OSCC management. Further research is warranted to elucidate the precise mechanisms underlying TLR3-mediated carcinogenesis in OSCC, facilitating the development of tailored therapeutic strategies.

The Role of Salivary Micro RNAs as a Diagnostic Marker in Early Detection of Oral Squamous Cell Carcinoma

OBJECTIVE: To assess the diagnostic role of salivary microRNA for early detection oforal squamous cell carcinoma and to compare the salivary microRNA with Biopsy in thediagnosis of oral squamous cell carcinoma.Methodology: The descriptive cross-sectional study was conducted in the department of pathology and molecular laboratory, department of oral &amp;amp; maxillofacial surgery of Liaquat University of Medical Health Sciences, Jamshoro / Hyderabad.Inclusion Criteria: All histopathologicaly diagnosed OSCC patients of both genders with age group 18 years and above will be included in the study after taking their informed consent. Whole saliva samples (inactivated) were collectedfrom subjects diagnosed as OSCC and controls. The subjects were counseled to avoid eating, drinking, smoking or oral procedures for at least 1 h prior to the collection of saliva. Subjects are asked to rinse their mouth well with distilled drinking water for one minute before taking the saliva samples. After five minutes of oral rinsing spit 5 mL of saliva into a 50 mL sterile tube placed on the ice. The tube should remain on ice while collecting the saliva samples. Fourhundred microliters of the whole saliva mixture (200 ?L whole saliva and 200 ?L RNA later),and 400 ?L of the supernatant saliva was used for RNA extraction. Saliva samples wereextracted using the mirVana™ miRNA Isolation Kit according to the manufacturer&amp;#39;s guideline(Ambion Inc., Austin, TX). Whole saliva samples were preserved with RN Alater (QIAGENInc., Valencia, CA) and supernatant saliva samples were preserved with SUPE Rase. In™ (Ambion Inc., Austin, TX). The data was entered in the statistical package for social sciences for windows (SPSS) V: 26.RESULTS: A total of 120 samples of oral squamous cell carcinoma were taken, which met theinclusion criteria. Mean age of the patients was 47.45+10.85 years. 85(70.8%) were married and35 (29.2%) were unmarried. 90.0% were married and 5.0% were unmarried, while 5.0% werewidow. Cigarette smoking, paan, chaalia and naswar were the commonest cause of the oralsquamous cell carcinoma. Lips, buccal mucosa and tong were the commonest t sites of the oralsquamous cell carcinoma. 16.7% cases had positive family history of oral squamous cellcarcinoma. Out of all study subjects, most of the cases 59.2% had moderately differentiatedSCC, 30.0% cases had well-differentiated SCC and 10.8% of the cases had poor differentiatedSCC. Out of all study subjects, 87.5% of the patients had positive micro-RNA expression.Micro-RNA expression was significantly associated, with poorly differentiated squamous cellcarcinoma (p=0.016). CONCLUSION: Study revealed that the salivary miroRNA expression were significantlypositive in patients of OSCC. Therefore, salivary microRNA could be considered as a useful andadvantageous biomarker for the detection and tracking of OSCC across various levels of tissueabnormalities

Evaluation of salivary IL-10 and IL-8 as predictive biomarker in patient with oral and oropharyngeal squamous cell carcinoma

Background: The incidence and mortality rates of oral squamous cell carcinoma (OSCC) vary globally, yet the cancer continues to pose significant morbidity and mortality risks. This study sought to assess salivary IL-8 and IL-10 as biomarkers for identifying malignant oral lesions. Methods: Between March 2022 and May 2023, a case-control study at the Dental Center of New Baquba, Diyala, Iraq, collected saliva for IL-8 and IL-10 measurement via ELISA. An independent t-test compared IL-8 and IL-10 means based on age and gender, while ROC analysis gauged specificity and sensitivity. Results: The prevalence of OSCC was evenly distributed across genders, with a higher occurrence among patients aged over 55 years (87.5%). OSCC distribution in the oral cavity indicated the lips as the most affected area (40.0%), followed by the tongue (37.0%), and the hard and soft palate (12.50%). Statistically significant differences were noted in IL-10 and IL-8 levels between the study and control groups across all age brackets, except for IL-8 in the age ≤55 (p ≤ 0.05). Elevated mean levels of IL-10 and IL-8 were observed in the older age group (&gt;55), with IL-10 showing a greater increase in males (71.99±3.6), while IL-8 levels were higher in females. IL-10 demonstrated higher sensitivity (90%) and specificity (60%) with an optimal cutoff point value of 43.29, whereas IL-8 exhibited a sensitivity of 80% and a specificity of 47.5% with an optimal cutoff point value of 44.48. Conclusion: Individuals aged over 55 showed higher IL-10 and IL-8 levels, particularly in males for IL-10 and in females for IL-8. Salivary biomarkers hold promise for early OSCC detection.

Application of metabolomics in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC. This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed. This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed. Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.