Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients - Xianqiang Liu; Dingchang Li; Yue Zhang; Hao Liu; Peng Chen; Yingjie Zhao; Guanchao Sun; Wen Zhao; Guanglong Dong (Biomedicines)
Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.