Department of General Education, Cangzhou Medical College, Cangzhou, 061001, China.
Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 252200, China.
Sci Rep. 2022 Jan 10;12(1):437. doi: 10.1038/s41598-021-04268-7.
Bioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database ( http://www.immport.org/ ); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan-Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.
生物信息学方法用于构建非小细胞肺癌（NSCLC）患者的免疫基因预后评估模型，并筛选影响 NSCLC 发生和预后的生物标志物。从癌症基因组图谱（TCGA）数据库下载 NSCLC 和癌旁正常组织的转录组数据和临床病理数据，并从 IMMPORT 数据库（http://www.immport.org/）获取免疫相关基因；然后筛选出差异表达的免疫基因。基于这些基因，构建免疫基因预后模型。采用 Cox 比例风险回归模型进行单因素和多因素分析。进一步分析风险评分与 NSCLC 的临床病理特征、肿瘤微环境和预后的相关性。基于 R 语言中的“wilcox.test”筛选出与 NSCLC 相关的 193 个差异表达的免疫基因，Cox 单因素分析显示 19 个差异表达的免疫基因与 NSCLC 的预后相关（P<0.05）。将 19 个差异表达的免疫基因（P<0.05）纳入 Cox 多因素分析后，构建了 NSCLC 的免疫基因预后模型（包含 13 个差异表达的免疫基因）。基于风险评分，将样本分为高风险组和低风险组。Kaplan-Meier 生存曲线结果显示，高风险组的 5 年总生存率为 32.4%，低风险组的 5 年总生存率为 53.7%。受试者工作特征模型曲线证实该预测模型具有一定的准确性（AUC=0.673）。将多个变量纳入 Cox 回归分析后，结果表明免疫基因预后风险评分是 NSCLC 患者预后的独立预测因子。风险评分与肿瘤微环境中中性粒细胞浸润程度之间存在一定的相关性。基于生物信息学方法构建了 NSCLC 免疫基因预后评估模型，可以计算 NSCLC 患者的预后风险评分。该模型有望为临床判断 NSCLC 患者的预后提供帮助。
