鉴定一个独立的免疫基因预后指标用于肾细胞癌。
Identification of an independent immune-genes prognostic index for renal cell carcinoma.
机构信息
Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
The Affiliated Kezhou People's Hospital of Nanjing Medical University, Kezhou, Xinjiang, 845350, China.
出版信息
BMC Cancer. 2021 Jun 29;21(1):746. doi: 10.1186/s12885-021-08367-6.
BACKGROUND
Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC.
METHODS
Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis.
RESULTS
Via differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients' survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways.
CONCLUSION
In general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.
背景
大量证据表明，免疫微环境与 ccRCC 的临床结局之间存在关联。本研究旨在深入探讨肿瘤免疫相关基因对 ccRCC 患者预后的影响。
方法
从免疫数据库和分析门户（ImmPort）中获取包含 2498 个免疫相关基因的文件，并从 TCGA 数据库中识别和下载与 ccRCC 患者相关的转录组数据和临床信息。使用单因素和多因素 Cox 回归分析筛选预后免疫基因。根据生存和枢纽免疫相关基因之间的回归系数建立免疫风险评分模型。最终建立了用于预测 ccRCC 总生存期的列线图。 Kaplan-Meier（K-M）和 ROC 曲线用于评估预测风险模型的价值。数据分析中 P 值<0.05 表示具有统计学意义差异。
结果
通过差异分析，我们发现肿瘤组织和正常组织之间有 556 个免疫相关基因表达差异（p<0.05）。单因素 Cox 回归分析表明，43 个免疫基因与 ccRCC 患者的生存风险存在统计学相关性（p<0.05）。通过 Lasso-Cox 回归分析，我们建立了一个基于 18 个免疫相关基因的免疫遗传风险评分模型。在 K-M 分析中，高风险组预后不良（p<0.001）。ROC 曲线表明，免疫风险评分模型预测生存风险的可靠性较高（5 年总生存率，AUC=0.802）。该模型在验证数据集（5 年 OS，AUC=0.705，p<0.05）中具有较好的 AUC 和生存相关性。从多因素回归分析来看，免疫风险评分模型在 ccRCC 预后预测中具有独立作用。在多因素 Cox 回归分析的基础上，我们建立了一个综合预测 ccRCC 患者生存率的列线图。最后，我们确定了 18 个免疫相关基因和风险评分不仅与临床预后密切相关，而且还包含在多种致癌途径中。
结论
总的来说，肿瘤免疫相关基因在 ccRCC 的发生和发展中起着重要作用。本研究建立了一个直观的 18 个免疫基因风险指数来预测 ccRCC 的预后。该指数被发现是 ccRCC 的一个独立预测因子。
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