Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma
Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma
复制标题
基于透明细胞肾细胞癌中表达的免疫相关基因的临床预后模型的开发和验证
DOI：
10.3389/fonc.2020.01496
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发表时间：
2020-08-28
影响因子：
4.7
通讯作者：
Wang, Ziheng
中科院分区：
文献类型：
--
作者：
Ren, Shiqi;Wang, Wei;Wang, Ziheng
背景：肾透明细胞癌（Clear cell renal cell carcinoma，ccRCC）是肾细胞癌中最常见的晚期亚型.与免疫应答相关的可靠标志物无法预测ccRCC患者的预后。我们利用了来自癌症基因组图谱（TCGA）和基因表达综合库（GEO）的大量ccRCC样本，对免疫相关基因（IRG）进行了全面分析。方法：基于TCGA数据，我们整合了72例正常和539例ccRCC样本的IRGs及其表达谱。单因素考克斯分析IRGs表达与总生存期（OS）的关系。Lasso考克斯回归模型鉴定了用于建立临床免疫预后模型的预后基因。TF-IRG网络用于研究ccRCC特异性IRG的潜在分子作用机制和性质。多因素考克斯分析建立了IRGs的临床预后模型。结果：我们发现15个差异表达的IRGs与ccRCC患者的OS显著相关。基因功能富集分析表明，这些IRGs与受体配体活性反应显著相关。Lasso考克斯回归分析确定了10个具有最大预后价值的基因。基于六个IRG的临床预后模型，其在预测预后方面表现良好，显示患者的生存与年龄、性别、分期、肿瘤、淋巴结和转移显著相关。此外，这些发现反映了各种免疫细胞对肿瘤的浸润。结论：我们确定了6个具有临床意义的IRG，并将其纳入临床预后模型，对监测和预测ccRCC的预后具有重要意义。
Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF–IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs. Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells. Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC.