目的 基于TCGA数据库,结合人类自噬数据库构建预测肺腺癌患者预后的风险模型,探讨自噬相关基因预后风险模型对肺腺癌患者预后的预测性能及与免疫微环境的相关性.方法 从癌症基因组图谱数据库下载肺腺癌患者临床信息和转录组数据,结合人类自噬数据库筛选出232个自噬相关基因.通过Cox回归分析筛选出4个独立与预后相关的自噬基因,并采用风险评分构建肺腺癌预后预测模型,用ROC曲线评价预测模型性能.使用ESTIMATE和CIBERSORT在线网站(https://cibersort.stanford.edu/)探索风险评分与肿瘤免疫微环境之间的关系.结果 肺腺癌中有30个差异表达的自噬相关基因,其中4个自噬基因(BIRC5、ERO1A、ITGB4、NLRC4)具有预测患者预后的功能.依据风险评分进行分组,Kaplan-Meier分析表明高风险组生存率低于低风险组(P<0.0001).ROC曲线表明风险评分模型判断肺腺癌预后的准确性(AUC=0.757).ESTIMATE和CIBERSORT分析提示风险评分模型与肿瘤微环境中的多个免疫细胞亚群浸润相关.结论 自噬相关基因预后风险模型较临床数据能更好地预测肺腺癌患者预后.在高风险组中,CD4+记忆静止细胞可改善肺腺癌患者预后.

OBJECTIVE:Patients with lung cancer are at risk of radiation pneumonia (RP) after receiving radiotherapy. We established a prediction model according to the critical indicators extracted from radiation pneumonia patients.MATERIALS AND METHODS:74 radiation pneumonia patients were involved in the training set. Firstly, the clinical data, hematological and radiation dose parameters of the 74 patients were screened by Logistics regression univariate analysis according to the level of radiation pneumonia. Next, Stepwise regression analysis was utilized to construct the regression model. Then, the influence of continuous variables on RP was tested by smoothing function. Finally, the model was externally verified by 30 patients in validation set and visualized by R code.RESULTS:In the training set, there was 40 patients suffered≥ level 2 acute radiation pneumonia. Clinical data (diabetes), blood indexes (lymphocyte percentage, basophil percentage, platelet count) and radiation dose (V15 > 40%, V20 > 30%, V35 >18%, V40 > 15%) were related to radiation pneumonia (P < 0.05). Particularly, stepwise regression analysis indicated that the history of diabetes, the basophils percentage, platelet count and V20 could be the best combination used for predicting radiation pneumonia. The column chart was obtained by fitting the regression model with the combined indicator. The receiver operating characteristic (ROC) curve showed that the AUC in the development term was 0.853, the AUC was 0.656 in the validation term. And calibration curves of both groups showed the high stability in efficiently diagnostic. Furthermore, the DCA curve showed that the model had a satisfactory positive net benefit.CONCLUSION:The combination of the basophils percentage, platelet count and V20 is available to build a predictive model of radiation pneumonia for patients with advanced lung cancer.

BACKGROUND:The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. MATERIALS AND METHODS:We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. RESULTS:We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). CONCLUSION:We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.