Background Epigenetic reprogramming is involved in multiple steps of human cancer evolution and is mediated by a variety of chromatin-modifying enzymes. Specifically, the histone lysine methyltransferase KMT2D is among the most frequently mutated genes in oral squamous cell carcinoma (OSCC). However, the mechanisms by which KMT2D affects the development of OSCC remain unclear. Results In the present study, we found that the expression of KMT2D was elevated in OSCC compared to paracancerous specimens and was correlated with a more advanced tumor grade. More importantly, knockdown of KMT2D impaired their reconstitution in patient-derived organoids and decreased the expression of CD133 and β-catenin in OSCC cells. In in vitro and in vivo models, knockdown of KMT2D reduced the colony formation, migration and invasion abilities of OSCC cells and delayed tumor growth. Mechanistically, the dual-luciferase reporter and co-immunoprecipitation assays in two individual OSCC cell lines indicated that KMT2D may cooperate with MEF2A to promote the transcription activity of CTNNB1, thereby enhancing WNT signaling. Conclusion The upregulation of KMT2D contributes to stem-like properties in OSCC cells by sustaining the MEF2A-mediated transcriptional activity of CTNNB1.

Oral squamous cell carcinoma (OSCC) therapy is unsatisfactory, and the prevalence of the disease is increasing. The role of mitochondria in OSCC therapy has recently attracted increasing attention, however, many mechanisms remain unclear. Therefore, we elaborate upon relative studies in this review to achieve a better therapeutic effect of OSCC treatment in the future. Interestingly, we found that mitochondria not only contribute to OSCC therapy but also promote resistance, and targeting the mitochondria of OSCC via nanoparticles is a promising way to treat OSCC.

Objectives Lymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy. Methods The preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected. In stage I, full-layer images (five common anatomical structures) were labeled; in stage II, negative and positive LNs were separately labeled. The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. The accuracy of the model was compared with that of human observers. Results A total of 5412 images and 5601 images were labeled in stage I and II, respectively. The stage I model achieved an excellent segmentation effect in the test set (AP50-0.7249). The positive LN accuracy of the stage II TL model was similar to that of the radiologist and much higher than that of the surgeons and students (0.7042 vs. 0.7647 (p = 0.243), 0.4216 (p < 0.001), and 0.3629 (p < 0.001)). The clinical accuracy of the model was highest (0.8509 vs. 0.8000, 0.5500, 0.4500, and 0.6658 of the Radiology Department). Conclusions The model was constructed using a deep neural network and had high accuracy in LN localization and metastasis discrimination, which could contribute to accurate diagnosis and customized treatment planning. Key Points • Lymph node metastasis is not well recognized with modern medical imaging tools. • Transfer learning can improve the accuracy of deep learning model prediction. • Deep learning can aid the accurate identification of lymph node metastasis.

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