背景:一小部分患者可以从免疫治疗中获得实质性益处,主要归因于免疫逃避。我们的目标是制定一个与细胞毒性T淋巴细胞逃避(CERGs)相关的基因,目的是预测结直肠癌(CRC)的结局和辨别免疫治疗反应。
方法:在交叉验证框架下,应用101种机器学习算法组合来计算CERGs预后指数(CERPI),将CRC患者分为高CERPI组和低CERPI组。免疫细胞浸润水平之间的关系,免疫相关评分,进一步分析了恶性表型和CERPI.使用各种机器学习方法来识别与患者生存和免疫疗法益处相关的关键基因。评估HOXC6、G0S2和MX2的表达,并在体外验证HOXC6和G0S2对CRC细胞系的活力和迁移的影响。
结果:CERPI在预测CRC患者的总生存期方面显示出强大的预后功效,将自己确立为患者预后的独立预测因子。低CERPI组的免疫细胞浸润水平升高,肿瘤免疫功能障碍和排斥评分降低,表明免疫疗法有更大的潜在益处。此外,CERPI水平与恶性肿瘤表型呈正相关,提示升高的CERPI表达有助于肿瘤的发生和进展.确定了13个关键基因,通过对单细胞数据集的分析,对它们的表达模式进行了仔细检查。值得注意的是,HOXC6、G0S2和MX2在CRC细胞系和组织中均表现出上调。随后靶向G0S2和HOXC6的敲低实验导致CRC细胞活力和迁移的显著抑制。
结论:我们开发了CERPI用于有效预测患者的生存率和免疫治疗反应,这些结果可为CRC的诊断和精准治疗提供指导。
BACKGROUND: A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC).
METHODS: 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified.
RESULTS: The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration.
CONCLUSIONS: We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.