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.