生物信息学分析肿瘤浸润免疫细胞与视网膜母细胞瘤预后的关系
Bioinformatics Analysis of the Relationship between Tumor Infiltrating Immune Cells and Prognosis in Retinoblastoma

目的：探讨肿瘤浸润免疫细胞(TIICs)在视网膜母细胞瘤(RB)患者预后中的作用，并构建基于多种免疫细胞的预后模型。方法：从GEO数据库获取RB样本和正常对照样本的基因表达谱及生存信息。(1) 使用CIBERSORT算法计算样本中不同免疫细胞的比例，进行免疫细胞浸润分析；(2) 进行Kaplan-Meier生存分析；(3) 通过LASSO回归、单因素和多因素Cox回归筛选与预后相关的免疫细胞，最终基于训练队列构建由3种免疫细胞(静息NK细胞、M0巨噬细胞和未活化CD4 T细胞)组成的预后模型；(4) 构建免疫细胞风险评分模型，并用Kaplan-Meier法评估模型；(5) 通过ROC曲线评估模型的预测能力。结果：在肿瘤样本中，树突状激活细胞、静息CD4记忆T细胞、调节性T细胞等免疫细胞活跃，而在正常样本中，初始B细胞和肥大细胞较为活跃。生存分析显示，低风险组患者的生存期显著长于高风险组。M0巨噬细胞和未活化CD4 T细胞与生存时间显著相关(P < 0.05)。结论：TIICs在RB的肿瘤微环境中起重要作用，特定免疫细胞(如M0巨噬细胞、未活化CD4 T细胞和静息NK细胞)与患者生存期显著相关。本研究构建的预后模型为临床预后评估提供了新的参考。
Objective: To investigate the role of tumor infiltrating immune cells (TIICs) in the prognosis of retinoblastoma (RB) patients, and to construct a prognostic model based on a variety of immune cells. Methods: The gene expression profiles and survival information of RB samples and normal control samples were obtained from GEO database. (1) The CIBERSORT algorithm was used to calculate the proportion of different immune cells in the sample, and the immune cell infiltration analysis was carried out; (2) Kaplan-Meier survival analysis; (3) Immune cells related to prognosis were screened by LASSO regression, univariate and multivariate Cox regression, and finally a prognostic model composed of three immune cells (resting NK cells, M0 macrophages and inactivated CD4 T cells) was constructed based on the training cohor; (4) Construct an immune cell risk scoring model and evaluate the model by Kaplan-Meier method; (5) The predictive ability of the model was evaluated by the ROC curve. Results: In tumor samples, immune cells such as dendritic activating cells, resting CD4 memory T cells, and regulatory T cells were active, while na？ve B cells and mast cells were more active in normal samples. Survival analysis showed that patients in the low-risk group had significantly longer survival than those in the high-risk group. M0 macrophages and unactivated CD4 T cells were significantly correlated with survival time (P < 0.05). Conclusion: TIICs play an important role in the tumor microenvironment of RB, and specific immune cells (such as M0 macrophages, unactivated CD4 T cells, and resting NK cells) are significantly associated with patient survival. The prognosis model constructed in this study provides a new reference for clinical prognosis evaluation.