Sun Bincan, Gan Chengwen, Tang Yan, Xu Qian, Wang Kai, Zhu Feiya
Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, Hunan, P. R. China.
Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, Hunan, P. R. China.
Cancer Cell Int. 2024 Oct 26;24(1):350. doi: 10.1186/s12935-024-03543-7.
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.
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. To facilitate the assessment of individual prognostic outcomes, we further constructed a nomogram based on the risk score and other clinical factors.
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 TLS 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. The nomogram demonstrates excellent predictive ability for overall survival in OSCC patients.
This is the first prognostic nomogram based on TLSRGs, that can effectively predict survival outcomes and contribute to individual treatment strategies for OSCC patients.
三级淋巴结构（TLSs）具有免疫调节功能，对口腔鳞状细胞癌（OSCC）患者的生存结局有积极影响。然而，目前缺乏用于量化TLSs的标准方法以及使用TLS相关基因（TLSRGs）的预后模型。这些局限性限制了TLSs在临床实践中的广泛应用。
使用卷积神经网络自动检测和量化苏木精-伊红（HE）染色的全玻片图像中的TLSs。通过生物信息学和多种统计方法，本研究利用癌症基因组图谱（TCGA）队列创建了一个预后模型，并探索了该模型与免疫浸润之间的联系。通过免疫组织化学检测临床标本中三种TLSRGs的表达水平。为便于评估个体预后结果，我们进一步基于风险评分和其他临床因素构建了列线图。
发现TLSs是OSCC患者总生存期（OS）和无病生存期的独立预测因子。TLS面积占比越大，预后越好。经过分析，我们确定了69个差异表达的TLSRGs，并选择了三个关键的TLSRGs来构建风险评分模型。该模型成为OS的独立预测因子，并与CD4 + T细胞、CD8 + T细胞和巨噬细胞密切相关。免疫组织化学显示，TLS + OSCC样本中CCR7和CXCR5表达水平较高，而TLS - OSCC样本中CD86表达水平较高。列线图对OSCC患者的总生存期具有出色的预测能力。
这是首个基于TLSRGs的预后列线图，可有效预测生存结局，为OSCC患者的个体化治疗策略提供依据。
