Following the publication of this paper, it was drawn to the Editor\'s attention by a concerned reader that the colony formation assay data shown in Fig. 4C on p. 6 were strikingly similar to data appearing in different form in other articles written by different authors at different research institutes, which had already been published. Owing to the fact that the contentious data in the above article had already been published prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. After having been in contact with the authors, they accepted the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 24: 685, 2021; DOI: 10.3892/mmr.2021.12325].

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors.
METHODS: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.
RESULTS: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model\'s performance, increasing the validation cohort AUC to 0.87.
CONCLUSIONS: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.
CONCLUSIONS: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.

BACKGROUND: Accurate regulation of gene expression is crucial for normal development and function of cells. The prognostic significance and potential carcinogenic mechanisms of the related gene JARID2 in OSCC are not yet clear, but existing research has indicated a significant association between the two.
METHODS: The relationship between the expression of the JARID2 gene in tumor samples of OSCC patients and clinical pathological factors was analyzed using immunohistochemistry experiments and RT-qPCR analysis. Based on the clinical pathological data of patients, bioinformatics analysis was conducted using public databases to determine the function of JARID2 in OSCC. Knockdown OSCC cell lines were constructed, and the impact of JARID2 on the biological behavior of OSCC cell lines was assessed through CCK-8, wound healing assay, and transwell analysis.
RESULTS: Immunohistochemistry experiments confirmed the correlation between JARID2 and the prognosis of OSCC patients, while RT-qPCR experiments demonstrated its expression levels in tissue and cells. CKK-8 experiments, wound healing assays, and Transwell experiments indicated that knocking down JARID2 had a negative impact on the proliferation, invasion, and migration of OSCC cells. Bioinformatics analysis results showed that the expression of JARID2 in OSCC is closely associated with patient gene co-expression, gene function enrichment, immune infiltration, and drug sensitivity.
CONCLUSIONS: Our study indicates that JARID2 is a novel prognostic biomarker and potential therapeutic target for OSCC.

In 1977, the American Joint Committee on Cancer (AJCC) introduced the inaugural Cancer Staging Manual, which implemented the T (tumor extent), N (regional lymph node status), and M (presence or absence of distant metastasis) staging system. This systematic approach aimed to convey the extent of disease across various cancer types, providing clinicians with a practical framework to plan treatment strategies, predict prognosis, and assess outcomes. The AJCC 8th edition, effective from January 1, 2018, continues this tradition. However, certain shortcomings persist in the AJCC 8th edition, as identified through clinical experience. Specifically, challenges arise in accurately assessing depth of invasion in unique histological variants of oral squamous cell carcinoma (e.g., Oral verrucous carcinoma, Carcinoma cuniculatum, and Papillary squamous cell carcinoma) and minor salivary gland tumors. Additionally, discrepancies exist in the perception of bone invasion patterns and in reporting practices. There is also a need for staging guidelines for malignant odontogenic tumors and multifocal tumors of the oral cavity, supplemented by diagrammatic representations. Lastly, there is a call for comprehensive staging criteria for carcinomas of the ear, external auditory canal, and temporal bone. We advocate for the inclusion of these considerations in future editions of the AJCC Cancer Staging Manual.

UNASSIGNED: The performance of artificial intelligence (AI) in the prediction of lymph node (LN) metastasis in patients with oral squamous cell carcinoma (OSCC) has not been quantitatively evaluated. The purpose of this study was to conduct a systematic review and meta-analysis of published data on the diagnostic performance of CT and MRI based on AI algorithms for predicting LN metastases in patients with OSCC.
UNASSIGNED: We searched the Embase, PubMed (Medline), Web of Science, and Cochrane databases for studies on the use of AI in predicting LN metastasis in OSCC. Binary diagnostic accuracy data were extracted to obtain the outcomes of interest, namely, the area under the curve (AUC), sensitivity, and specificity, and compared the diagnostic performance of AI with that of radiologists. Subgroup analyses were performed with regard to different types of AI algorithms and imaging modalities.
UNASSIGNED: Fourteen eligible studies were included in the meta-analysis. The AUC, sensitivity, and specificity of the AI models for the diagnosis of LN metastases were 0.92 (95% CI 0.89-0.94), 0.79 (95% CI 0.72-0.85), and 0.90 (95% CI 0.86-0.93), respectively. Promising diagnostic performance was observed in the subgroup analyses based on algorithm types [machine learning (ML) or deep learning (DL)] and imaging modalities (CT vs. MRI). The pooled diagnostic performance of AI was significantly better than that of experienced radiologists.
UNASSIGNED: In conclusion, AI based on CT and MRI imaging has good diagnostic accuracy in predicting LN metastasis in patients with OSCC and thus has the potential for clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#recordDetails, PROSPERO (No. CRD42024506159).

BACKGROUND: Oral squamous cell carcinoma (OSCC), while common and with a favorable prognosis in early stages, presents a marked reduction in survival rate upon metastasis to lymph nodes. Early detection of lymph node metastasis via biomarkers could enhance the therapeutic strategy for OSCC. Here, we explored dendritic cells (DCs) and cytotoxic T-cells in tumour-draining lymph nodes (TDLNs) as potential biomarkers.
METHODS: Dendritic cells and cytotoxic T-cells in 33 lymph nodes were analyzed with multi-parameter flow cytometry in TDLNs, regional non-TDLNs surgically excised from 12 OSCC patients, and compared to 9 lymph nodes from patients with benign conditions.
RESULTS: Our results displayed a higher proportion of conventional cDC1s with immunosuppressive features in TDLN. Further, high PD-L1 expression on cDC1 in TDLNs was associated with metastasis and/or recurrent disease risk. Also, elevated levels of memory CD8+ T-cells and terminally exhausted PD-1+TCF-1-CD8+ T-cells were observed in TDLNs and non-TDLNs compared to healthy lymph nodes.
CONCLUSIONS: We conclude that TDLNs contain cells that could trigger an anti-tumor adaptive response, as evidenced by activated cDC1s and progenitor-like TCF-1+ T-cells. The detection of high PDL1 expression on cDC1s was indicative of TDLN metastasis and an adverse prognosis, proposing that PD-L1 on dendritic cells in TDLN could serve as a predictive biomarker of OSCC patients with a worse prognosis.

OBJECTIVE: To find efficient cuproptosis-related biomarkers to explore the oncogenesis and progression of oral squamous cell carcinoma (OSCC).
METHODS: All the original data were downloaded from the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and Kaplan-Meier survival analysis were used to identify the gene related to survival. Tumor Immune Estimation Resource 2.0 (TIMER 2.0) was used to reveal the different expression of cuproptosis-related gene lipoyltransferase 1 (LIPT1) in various kinds of tumours.
RESULTS: LIPT1, as a cuproptosis-related gene, was found to be differentially expressed in the OSCC group and the control group. It was also found to be related to the prognosis of OSCC. Pan cancer analysis showed LIPT1 was also involved in various kinds of tumours.
CONCLUSIONS: All the results demonstrate that the cuproptosis-related gene LIPT1 is highly involved in the oncogenesis and progression of OSCC. These findings give new insight for further research into the cuproptosis-related biomarkers in OSCC.

An association between periodontal disease and oral squamous cell carcinoma (OSCC) has been recognized. However, there is no causal relationship between the two. The polymicrobial etiology of periodontal disease is confirmed, and so are the proven etiological factors for OSCC. Inflammation lies at the core of periodontal pathogenesis induced by the putative microbes. OSCC has inflammatory overtures in its pathobiology. Bacterial species involved in periodontal disease have been extensively documented and validated. The microbial profile in OSCC has been explored with no specific conclusions. The scientific reasoning to link a common microbial signature that connects periodontal disease to OSCC has led to many studies but has not provided conclusive evidence. Therefore, it would be beneficial to know the status of any plausible microbiota having a similarity in periodontal disease and OSCC. This brief review attempted to clarify the existence of a dysbiotic \"fingerprint\" that may link these two diseases. The review examined the literature with a focused objective of identifying periodontal microbial profiles in OSCC that could provide insights into pathogen commonality. The review concluded that there is great diversity in microbial association, but important bacterial species that correlate with periodontal disease and OSCC are forthcoming.

Chemotherapy resistance is a major obstacle to effective cancer treatment, and promotion of ferroptosis can suppress cisplatin resistance in tumor cells. TCF12 plays a suppressive role in oral squamous cell carcinoma (OSCC), but whether it participates in the regulation of cisplatin resistance by modulating ferroptosis remains unclear. Here, we found that TCF12 expression was decreased in OSCC cells compared with normal oral cells, and it was reduced in cisplatin (DDP)-resistant OSCC cells compared with parental cells. Moreover, overexpression of TCF12 sensitized DDP-resistant cells to DDP by promoting ferroptosis. Intriguingly, silencing TCF12 reversed the promotion effect of the ferroptosis activator RSL3 on ferroptosis and DDP sensitivity, and overexpressing TCF12 antagonized the effect of the ferroptosis inhibitor liproxstatin-1 on ferroptosis and DDP resistance. Mechanically, TCF12 promoted ubiquitination of SLC7A11 and decreased SLC7A11 protein stability through transcriptional repression of OTUB1, thereby facilitating ferroptosis. Consistently, SLC7A11 overexpression neutralized the promotion effect of TCF12 on ferroptosis and DDP sensitivity. Additionally, upregulation of TCF12 hindered the growth of mouse OSCC xenografts and enhanced the DDP sensitivity of xenografts by inducing ferroptosis. In conclusion, TCF12 enhanced DDP sensitivity in OSCC cells by promoting ferroptosis, which was achieved through modulating SLC7A11 expression via transcriptional regulation of OTUB1.

BACKGROUND: ANXA5, a notable tumor marker, displays irregular expression in diverse solid cancers, and links to local recurrence and metastasis rates. We aimed study the expression of ANXA5 in oral squamous cell carcinoma (OSCC) and its diagnostic and prognostic values.
METHODS: 520 head and neck squamous cell carcinoma (HNSCC) patients in TCGA database and 124 OSCC patients in Nanjing stomatology hospital were enrolled in our study. Immunohistochemical analyses were performed using ANXA5 antibodies. Chi-square test was used to analyze the clinicopathological features. Survival rates were determined using the Kaplan-Meier method and log-rank test.
RESULTS: Our results showed significantly elevated ANXA5 at the gene and protein levels in HNSCC and OSCC compared to non-tumor tissues. Histopathologically, ANXA5 was broadly present in OSCC tumor cells and fibroblast-like cells but absent in tumor-infiltrating lymphocytes, particularly at the invasive tumor front. Patients exhibiting high ANXA5 expression in these cells demonstrated poor differentiation, aggressive invasion patterns, and heightened lymph node metastasis risk, contributing to poorer postoperative outcomes. Remarkably, ANXA5 in fibroblast-like cells emerged as an independent risk factor impacting survival in OSCC patients. Gene set enrichment analysis (GSEA) highlighted ANXA5\'s involvement in key pathways like epithelial-mesenchymal transformation (EMT), TGF-beta signaling, and hypoxia, which correlated with adverse clinical outcomes in OSCC.
CONCLUSIONS: ANXA5 emerges as a significant prognostic biomarker for OSCC, potentially influencing its metastasis via the EMT pathway.