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Abstract Background: The tertiary lymphoid structures (TLSs) have an immunomodulatory function and have a positive impact on the survival outcomes of patients with oral squamous cell carcinoma (OSCC). However, there is a lack of standard approaches for quantifying TLSs and prognostic models using TLS-related genes (TLSRGs). These limitations limit the widespread use of TLSs in clinical practice. Methods: A convolutional neural network was used to automatically detect and quantify TLSs in HE-stained whole slide images. By employing bioinformatics and diverse statistical methods, this research created a prognostic model using TCGA cohorts, and explored the connection between this model and immune infiltration. The expression levels of three TLSRGs in clinical specimens were detected by immunohistochemistry. Results: TLSs were found to be an independent predictor of both overall survival (OS) and disease-free survival in OSCC patients. A larger proportion of the TLSs area represented a better prognosis. After analysis, we identified 69 differentially expressed TLSRGs, and selected three pivotal TLSRGs to construct the risk score model. This model emerged as a standalone predictor for OS and exhibited close associations with CD4+ T cells, CD8+ T cells, and macrophages. Immunohistochemistry revealed high expression levels of CCR7 and CXCR5 in TLS+OSCC samples, while CD86 was highly expressed in TLS-OSCC samples. Conclusions: This is the first prognostic model based on TLSRGs, that can effectively predict survival outcomes and contribute to individual treatment strategies for OSCC patients.
Sun et al. (Fri,) studied this question.