基于GEO数据库分析影响纳武单抗及派姆单抗治疗非小细胞肺癌疗效的差异基因

Analysis of differential genes that affect the efficacy of Nivolumab and Pembrolizumab in the treatment of non-small cell lung cancer based on GEO database

目的 筛选影响纳武单抗和派姆单抗治疗非小细胞肺癌(non-small cell lung cancer, NSCLC)疗效的差异基因,为免疫治疗药物的选择及治疗预后提供参考。方法 通过GEO数据库搜索“Nivolumab”、“Pembrolizumab”找到目的芯片,下载免疫治疗相关表达芯片“GSE93157”,筛选NSCLC相关样本共35个,利用R语言数据包对样本进行表达差异基因进行聚类分析。对差异基因进行基因功能注释GO分析和KEGG通路分析,构建蛋白相互作用网络,筛选枢纽基因进行生存分析,确定影响不同抗程序性细胞死亡蛋白1药物治疗的关键基因。结果 筛选出影响纳武单抗治疗疗效差异基因共58个,其中免疫相关基因25个;影响派姆单抗治疗疗效差异基因231个,免疫相关基因82个。基于两种药物免疫相关差异基因的蛋白互作网络提示纳武单抗共得到2个子网络,主要模块共11个节点,51个边;派姆单抗共得到4个子网络,主要模块共24个节点,231个边。影响两种药物治疗疗效的前10位主要免疫相关基因生存分析,显示生存差异具有统计学意义(P<0.05)的基因,与纳武单抗相关的免疫差异基因为CD5、CD22、CR2、CD40LG。与派姆单抗相关的免疫差异基因为CTLA4、SELL、IL7、CD40LG、CD2、IL7R。结论 纳武单抗治疗NSCLC患者的关键免疫相关差异基因CD5、CD22、CR2;派姆单抗治疗NSCLC的关键免疫相关差异基因为CTLA4、SELL、IL7、CD2、IL7R。两种药物共同免疫相关差异基因为CD40LG,有望成为抗PD-1抑制剂治疗NSCLC预后预测的潜在生物标志物及靶点。

Objective Using gene expression microarray database and bioinformatics methods to screen the differential genes that affect the efficacy of Nivolumab and Pembrolizumab in the treatment of non-small cell lung cancer, to provide references for the selection of these two commonly used immunotherapy drugs in clinical practice and the prognosis of treatment. Methods Searching "Nivolumab" and "Pembrolizumab" through the GEO database to find the target chip, downloading the immunotherapy-related expression chip "GSE93157", screening out a total of 35 non-small cell lung cancer-related samples, and using the R language data package to perform differential gene expression and analyzing and performing cluster analysis on differential genes. Performing gene function annotation GO analysis and KEGG pathway analysis on differential genes, constructing protein interaction network, screening out pivot genes for survival analysis, and identifing key genes that affect different anti-programmed cell death protein 1 drug treatments. Results By analyzing the differential genes of NSCLC patients with different therapeutic effects(clinical benefits and disease progression) of Nivolumab or Pembrolizumab, a total of 58 differential genes were screened out that affect the therapeutic efficacy of Nivolumab or Pembrolizumab, including immune-related genes 25;231 differential genes and 82 immune-related genes that affect the therapeutic effect of Pembrolizumab. These differential genes are involved in different biological processes. The protein interaction network based on two kinds of drug immune-related differential genes prompted a total of 2 sub-networks for Nivolumab, with a total of 11 nodes and 51 edges for the main modules;for Pembrolizumab, a total of 4 sub-networks, the main module There are a total of 24 nodes and 231 edges. The survival analysis of the top 10 main immune-related genes that affect the efficacy of the two drugs was conducted, and genes with statistically significant differences in survival(P<0.05) were selected. The results showed that the immune differential genes related to Nivolumab were CD5, CD22, CR2, CD40LG. The immune differential genes related to Pembrolizumab are CTLA4, SELL, IL7, CD40LG, CD2, IL7R. Conclusion We analyzed the key immune-related differential genes CD5, CD22, and CR2 for Nivolumab in the treatment of NSCLC patients through bioinformatics;the key immune-related differential genes for Pembrolizumab in the treatment of NSCLC patients are CTLA4, SELL, IL7, CD2 IL7R. The two drugs share the common immune-related differential gene CD40LG. They are expected to become potential biomarkers and targets for predicting the efficacy of anti-PD-1 inhibitors in the treatment of NSCLC patients.