Liu Xianqiang, Li Dingchang, Zhang Yue, Liu Hao, Chen Peng, Zhao Yingjie, Sun Guanchao, Zhao Wen, Dong Guanglong
Medical School of Chinese PLA, Beijing 100853, China.
Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
Biomedicines. 2024 Nov 19;12(11):2644. doi: 10.3390/biomedicines12112644.
Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response.
TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model's predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model's potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression.
A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions.
The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC.
结直肠癌（CRC）是一种常见的恶性肿瘤，生存率低且对免疫治疗的反应率低。本研究旨在开发一种基于三级淋巴结构（TLS）相关基因特征的风险模型，以增强对预后和免疫治疗反应的预测。
从TCGA-CRC和GEO队列中获取TLS相关基因数据。使用单变量Cox回归进行综合分析，确定具有显著预后意义的TLS相关基因。随后，采用多种算法选择最具影响力的基因，并构建逐步Cox回归模型。使用独立数据集（GSE39582、GSE17536和GSE38832）验证该模型的预测性能。为进一步研究免疫微环境，使用CIBERSORT和ssGSEA算法评估高风险（HRG）和低风险（LRG）组中的免疫细胞浸润情况。此外，我们使用来自癌症影像存档的数据、TIDE算法和外部免疫治疗队列（GSE35640、GSE78200和PRJEB23709）评估该模型预测免疫检查点阻断治疗反应的潜力。采用免疫组织化学（IHC）来表征TLS的存在和CCL2基因表达。
一个由三个基因（CCL2、PDCD1和ICOS）组成的TLS相关模型被确定与预后密切相关，并对CRC患者的预后和免疫治疗疗效具有预测能力。值得注意的是，与高风险组（HRG）相比，低风险组（LRG）患者的总生存率更高，对免疫治疗的再反应率也更高。最后，IHC结果证实TLS区域中CCL2表达显著升高。
多算法整合模型在预测患者预后和免疫治疗反应方面表现出强大的性能，为评估免疫治疗疗效提供了新的视角。CCL2可能作为TLS调节剂发挥作用，并有望成为CRC的治疗靶点。
