IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis
IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis
复制标题
IRGS：肺腺癌预后的免疫相关基因分类器
DOI：
10.1186/s12967-020-02233-y
复制
发表时间：
2020-02-04
影响因子：
7.4
通讯作者：
Cai, Kaican
中科院分区：
文献类型：
--
作者：
Shi, Xiaoshun;Li, Ruidong;Cai, Kaican
背景肿瘤细胞通过影响某些免疫相关基因的表达而干扰正常的免疫功能，这些基因在癌症患者的预后中起着重要作用。近年来，肿瘤的免疫治疗得到了广泛的研究，但基于免疫相关基因预测肺腺癌预后的实用模型尚未建立和报道。方法我们首先从癌症基因组图谱(TCGA)中获取可公开获取的肺腺癌RNA表达数据，进行基因差异表达分析，然后基于ImmPort数据库筛选免疫相关基因。通过使用套索算法和多变量Cox比例风险(CoxPH)回归分析，我们确定了用于模型开发和验证的候选基因。通过与已建立的三个基因模型的比较，进一步检验了模型的稳健性。结果使用了来自TCGA的524例肺腺癌患者的基因表达数据来建立模型。我们确定了四个可以预测肺腺癌总体生存的生物标记物(MAP3K8、CCL20、VEGFC和ANGPTL4)(HR = 1.98，95%CI 1.48~2.64，P = 4.19e−06)，该模型可作为评估低危和高危人群的分类标准。该模型用独立的微阵列数据进行了验证，与以前报道的肺腺癌预后基因表达特征具有很高的可比性。结论在本研究中，我们基于具有跨平台兼容性的免疫基因数据集，确定了一个实用且稳健的四基因预后模型。该模型对改善肺腺癌患者的TNM分期、预测患者的生存有潜在的价值。本研究提供了一种免疫相关基因预后模型的建立方法和免疫基因分类器的确定，为应用RNA测序和基因芯片兼容性预测肺腺癌的预后提供了一种新的方法。
BackgroundTumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.MethodsWe first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.ResultsGene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e−06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.ConclusionsIn this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma.ImpactThe study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.