Department of Thoracic Surgery, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai 519000, China.
Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai 519000, China.
Biomolecules. 2024 Jul 24;14(8):891. doi: 10.3390/biom14080891.
Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8 T cells significantly influences the prognosis and progression of tumor patients.
This study establishes a CRC immune risk model based on CD8 T cell-related genes. CD8 T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8 T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm.
These genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model's prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan-Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8 T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model's efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect.
This study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8 T-cell infiltration.
结直肠癌(CRC)是最常见的影响胃肠道的恶性肿瘤之一。CD8 T 细胞的浸润显著影响肿瘤患者的预后和进展。
本研究基于 CD8 T 细胞相关基因建立了 CRC 免疫风险模型。通过加权基因共表达网络分析(WGCNA)识别 CD8 T 细胞相关基因,并通过基因本体论(GO)和反应途径分析注释富集基因集。采用机器学习方法,包括最小绝对值收缩和选择算子(LASSO)算法和随机森林(RF),鉴定了与 CD8 T 细胞浸润相关的九个基因。使用 ssGSEA 算法评估 CRC 组织中免疫细胞的浸润水平。
这些基因为构建预后模型提供了基础。TCGA-CRC 样本模型的预测评分进行分类,并通过 Cox 回归分析和 Kaplan-Meier 曲线分析验证预测模型。值得注意的是,虽然风险评分较高的 CRC 组织中 CD8 T 细胞浸润水平升高,但它们也表现出免疫检查点基因的高表达。此外,在免疫亚群中比较微卫星不稳定性(MSI)和基因突变,发现了显著的基因变异,特别是 APC、TP53 和 TNNT1 的突变频率更高。最后,通过使用肿瘤免疫功能障碍和排除(TIDE)、免疫特征评分(IPS)和免疫逃逸相关分子标志物,验证了预测模型的效能。通过 CRC 的外部队列和膀胱癌免疫治疗队列验证了预测模型。使用定量实时聚合酶链反应(qRT-PCR)和 Western blot 评估肿瘤和相邻正常组织中 CLRN3 的表达水平。随后的体外和体内实验表明,CLRN3 敲低显著抑制 CRC 细胞的恶性生物学行为,而过表达则产生相反的效果。
本研究提出了一种新的 CRC 预后模型,为通过靶向 CD8 T 细胞浸润提高 CRC 患者的生存率提供了框架。