Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. The poorly understood molecular and cellular mechanisms underlying the pathogenesis of OSCC remain a subject of paramount importance. For epithelial dysplasia, invasion, and metastasis to occur, tumor cells require energy obtained from the mitochondria and phenotypic cellular changes in the actin cytoskeleton. Dynamin-related protein1 (Drp1) is one of the main mitochondrial proteins regulating the mitochondrial dynamics. Cortactin is an actin-binding protein that promotes the actin polymerization and rearrangement. The interplay between both proteins in OSCC remains elusive. The current study aimed to investigate the immunohistochemical (IHC) expression of Drp1 and cortactin in tissues revealing propagating OSCC cases.

The retrospective study was carried out on 35 formalin-fixed paraffin sections of nodal metastasizing OSCC cases selected from the Oncology Centre, Faculty of Medicine, Mansoura University archives from 2018 to 2023. Immunohistochemistry for Drp1 and cortactin was done. The immune reactivity of both proteins was evaluated using computer-assisted digital image analysis. Statistical analysis was performed to identify significant differences and correlations between both markers in tissues associated with progressing OSCC cases using Chi-Square, Monte Carlo, One-Way ANOVA, and Spearman tests. The p-value less than 0.05 was considered statistically significant.

Drp1 expression was statistically significant to grades of primary OSCC (p = 0.015), while insignificant to grades of epithelial dysplasia (p = 0.123) and metastatic lymph nodes (LNs) (p = 0.212). Statistically significant differences between dysplastic epithelium & primary tumor, dysplastic epithelium & metastatic LNs, and primary tumor and metastatic LNs were observed (p values were 0.014, 0.001, 0.034, respectively). On the other hand, Cortactin expression revealed no statistically significant differences across the three groups. However, statistically significant differences between dysplastic epithelium & primary tumor, dysplastic epithelium & metastatic LNs, and primary tumor and metastatic LNs were found (p values were 0.014, 0.001, 0.034, respectively). Moreover, the Spearman test presented a strong positive correlation between Drp1 and cortactin expression in the studied cases.

Expressions of both Drp1 and cortactin relatively explain their great role in the propagation and the carcinogenesis of OSCC.

Background/Objectives: Lung cancer is a leading cause of cancer-related mortalities, with early diagnosis crucial for survival. While biopsy is the gold standard, manual histopathological analysis is time-consuming. This research enhances lung cancer diagnosis through deep learning-based feature extraction, fusion, optimization, and classification for improved accuracy and efficiency. Methods: The study begins with image preprocessing using an adaptive fuzzy filter, followed by segmentation with a modified simple linear iterative clustering (SLIC) algorithm. The segmented images are input into deep learning architectures, specifically ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152), for feature extraction. The extracted features are fused using a deep-weighted averaging-based feature fusion (DWAFF) technique, producing ResNet-X (RN-X)-fused features. To further refine these features, particle swarm optimization (PSO) and red deer optimization (RDO) techniques are employed within the selective feature pooling layer. The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). A performance evaluation is performed using K-fold cross-validation with K values of 2, 4, 5, 8, and 10. Results: The proposed DWAFF technique, combined with feature selection using RDO and classification with MLP, achieved the highest classification accuracy of 98.68% when using K = 10 for cross-validation. The RN-X features demonstrated superior performance compared to individual ResNet variants, and the integration of segmentation and optimization significantly enhanced classification accuracy. Conclusions: The proposed methodology automates lung cancer classification using deep learning, feature fusion, optimization, and advanced classification techniques. Segmentation and feature selection enhance performance, improving diagnostic accuracy. Future work may explore further optimizations and hybrid models.

Oral cancer (OC) is the sixth most common cancer worldwide. There have been few studies on OC in high-risk populations. This study aimed to describe the clinical features, staging, grading, and risk factors in OC patients.

This cross-sectional study was conducted on 109 OC patients diagnosed and treated from April 2018 to May 2020. The patients were identified using eData.

The average age of the patients was 60.32 ± 12.4 years, and the male-to-female ratio was 2.9:1. The most common site for OC was the tongue (37.6%), and oral squamous cell carcinoma was the most common histopathology (84.4%). The most common clinical forms were verruca (37.6%) and erosive ulcers (33.9%). Most patients were in stage III or IV (71.6%). Average time of detection was 7.32 ± 17.12 months.

OC occurs most often in elderly people, males, and is diagnosed late. The main risk factors are smoking and consuming alcohol.

Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility. Only a few studies have been conducted in automating TILs quantification for OSCC, existing methods use score-based systems that focus only on tissue-level spatial analysis, overlooking essential cellular-level information and do not provide TILs infiltration subcategories required for determining OSCC grading. To address these limitations, we propose OralTILs-ViT, a novel joint representation learning framework that integrates cellular and tissue-level information. Our model employs two parallel encoders: one extracts cellular features from cellular density maps, while the other processes tissue features from H&E-stained tissue images. This dual-encoder approach enables OralTILs-ViT to capture complex tissue-cellular interactions, classifying TILs infiltration categories consistent with Broders' grading system-"Moderate to Marked", "Slight" and "None to Very Less." This approach reflects pathology practices and increases TILs classification accuracy. To generate cellular density maps, we introduce TILSeg-MobileViT, a multiclass segmentation model trained using a weakly supervised method, minimizing the need for manual annotation of cellular masks and overcoming the limitations of previous TILs assessment techniques. An extensive evaluation of our methodology demonstrates that OralTILs-ViT with the configuration (Adam, α = 0.001) outperforms existing approaches, achieving 96.37% accuracy, 96.34% precision, 96.37% recall, and a 96.35% F1 score. Furthermore, TOPSIS analysis confirms that our method ranks first across all TILs infiltration categories. In summary, our proposed methodology outperforms single modality-representation learning approaches for accurate and automated TILs classification.

This study aimed to identify specific therapeutic targets for oral squamous cell carcinoma (OSCC) that metastasize from breast cancer (BC) by using network pharmacology. The Gene Expression Omnibus for OSCC and BC served as the source of gene expression datasets and their analysis. Upregulated genes and the common intersecting genes of these cancers were determined along with that of the phytochemicals of F. sargassum to predict the pharmacological targets. Further, gene enrichment analysis revealed that their metastasis signature and metastasis targets were determined via a protein interaction network. Molecular docking and pharmacokinetic screening determined the potential therapeutic phytochemicals against the targets. The interaction network of 39 genes thus identified encoding proteins revealed HIF1A as a prominent metastasis target due to its high degree of connectivity and its involvement in cancer-related pathways. Molecular docking showed a strong binding affinity of isonahocol D2, a sargassum-derived compound with HIF1A, presenting a binding energy of -7.1 kcal/mol. Further, pharmacokinetic screening showed favorable ADME properties and molecular dynamics simulations showed stable interactions between isonahocol D2 and HIF1A, with significant stability over 100 ns. This study's results emphasized that isonahocol D2 is a promising therapeutic candidate against HIF1A in OSCC metastasized from breast cancer in translational medicine.

Submucosal invasion depth is a significant prognostic factor when assessing lymph node metastasis and cancer itself to plan proper treatment for the patient. Conventionally, oncologists measure the invasion depth by hand which is a laborious, subjective, and time-consuming process. The manual pathological examination by measuring accurate carcinoma cell invasion with considerable inter-observer and intra-observer variations is still challenging. The increasing use of medical imaging and artificial intelligence reveals a significant role in clinical medicine and pathology. In this paper, we propose an approach to study invasive behavior and measure the invasion depth of carcinoma from stained histopathology images. Specifically, our model includes adaptive stain normalization, color decomposition, and morphological reconstruction with adaptive thresholding to separate the epithelium with blue ratio image. Our method splits the image into multiple non-overlapping meaningful segments and successfully finds the homogeneous segments to measure accurate invasion depth. The invasion depths are measured from the inner epithelium edge to outermost pixels of the deepest part of particles in image. We conduct our experiments on skin melanoma tissue samples as well as on organotypic invasion model utilizing myoma tissue and oral squamous cell carcinoma. The performance is experimentally compared to three closely related reference methods and our method provides a superior result in measuring invasion depth. This computational technique will be beneficial for the segmentation of epithelium and other particles for the development of novel computer-aided diagnostic tools in biobank applications.