An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma.
An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma.
复制标题
预测肺腺癌患者总体生存率的免疫相关列线图模型
DOI：
10.1186/s12890-022-01902-6
复制
发表时间：
2022-03-30
影响因子：
3.1
通讯作者：
Piao H
中科院分区：
文献类型：
--
作者：
Sun J;Yan Y;Meng Y;Ma Y;Du T;Yu T;Piao H
肺腺癌约占所有原发性肺癌的40%;然而，死亡率仍然很高。成功预测进展和总（OS）时间将为临床医生提供更多的选择来管理这种疾病。 我们使用CIBERSORT、ImmuCellAI和ESTIMATE算法分析了来自癌症基因组图谱数据库的510例肺腺癌的RNA测序数据。通过这些数据，我们构建了6个免疫亚型，然后比较了这些免疫亚型之间的OS、免疫浸润水平和基因表达的差异。并将各亚型及免疫细胞浸润水平与预后进行相关性分析，引入lasso-cox方法建立免疫相关预后模型。最后，我们在另一个独立的队列中验证了该模型。C3免疫亚型肺腺癌患者生存期较长，而C1免疫亚型肺腺癌患者MUC 17和FLG基因突变率较高。多因素相关分析显示，免疫细胞浸润与总生存率密切相关。利用510例患者的数据，我们构建了一个由临床病理因素和免疫特征组成的诺模图预测模型。该模型产生了0.73的C指数，并使用验证集实现了0.844的C指数。通过本研究，我们构建了一个免疫相关的预后模型来指导肺腺癌的OS，并在另一个独立的共宿主中验证了它的价值。这些结果将有助于指导基于肿瘤免疫特征的肺腺癌治疗。在线版本包含补充材料，可通过10.1186/s12890-022-01902-6获得。
Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease. We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort. The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set. Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles. The online version contains supplementary material available at 10.1186/s12890-022-01902-6.