全面的生物信息学分析鉴定小细胞肺癌中的肿瘤微环境和免疫相关基因。
Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer.
机构信息
Department of Oncology Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
Department of Pathology, Guangdong Medical University, Dongguan 523808, Guangdong, China.
出版信息
Comb Chem High Throughput Screen. 2020;23(5):381-391. doi: 10.2174/1386207323666200407075004.
BACKGROUND
Tumor microenvironment (TME) cells play important roles in tumor progression. Accumulating evidence show that they can be exploited to predict the clinical outcomes and therapeutic responses of the tumor. However, the role of immune genes of TME in small cell lung cancer (SCLC) is currently unknown.
OBJECTIVE
To determine the role of immune genes in SCLC.
METHODS
We downloaded the expression profile and clinical follow-up data of SCLC patients from Gene Expression Omnibus (GEO), and TME infiltration profile data of 158 patients using CIBERSORT. The correlation between TME phenotypes, genomic features, and clinicopathological features of SCLC was examined. A gene signature was constructed based on TME genes to further evaluate the relationship between molecular subtypes of SCLC with the prognosis and clinical features.
RESULTS
We identified a group of genes that are highly associated with TME. Several immune cells in TME cells were significantly correlated with SCLC prognosis (p<0.0001). These immune cells displayed diverse immune patterns. Three molecular subtypes of SCLC (TMEC1-3) were identified on the basis of enrichment of immune cell components, and these subtypes showed dissimilar prognosis profiles (p=0.03). The subtype with the best prognosis, TMEC3, was enriched with immune activation factors such as oncogene M0, oncogene M2, T cells follicular helper, and T cells CD8 (p<0.001). The TMEC1 subtype with the worst prognosis was enriched with T cells CD4 naive, B cells memory and Dendritic cells activated cells (p<0.001). Further analysis showed that the TME was significantly enriched with immune checkpoint genes, immune genes, and immune pathway genes (p<0.01). From the gene expression data, we identified four TME-related genes, GZMB, HAVCR2, PRF1 and TBX2, which were significantly associated with poor prognosis in both the training set and the validation set (p<0.05). These genes may serve as markers for monitoring tumor responses to immune checkpoint inhibitors.
CONCLUSION
This study shows that TME features may serve as markers for evaluating the response of SCLC cells to immunotherapy.
背景
肿瘤微环境（TME）细胞在肿瘤进展中发挥重要作用。越来越多的证据表明，它们可以被用来预测肿瘤的临床结局和治疗反应。然而，TME 中的免疫基因在小细胞肺癌（SCLC）中的作用目前尚不清楚。
目的
确定免疫基因在 SCLC 中的作用。
方法
我们从基因表达综合数据库（GEO）下载了 SCLC 患者的表达谱和临床随访数据，并使用 CIBERSORT 下载了 158 名患者的 TME 浸润谱数据。分析了 TME 表型、基因组特征与 SCLC 临床病理特征的相关性。基于 TME 基因构建基因特征，进一步评估 SCLC 分子亚型与预后和临床特征的关系。
结果
我们鉴定出一组与 TME 高度相关的基因。TME 中的几种免疫细胞与 SCLC 预后显著相关（p<0.0001）。这些免疫细胞表现出不同的免疫模式。基于免疫细胞成分的富集，我们鉴定出 SCLC 的三个分子亚型（TMEC1-3），这些亚型显示出不同的预后特征（p=0.03）。预后最好的亚型 TMEC3 富含免疫激活因子，如癌基因 M0、癌基因 M2、滤泡辅助性 T 细胞和 CD8 阳性 T 细胞（p<0.001）。预后最差的 TMEC1 亚型富含 CD4 阳性初始 T 细胞、记忆 B 细胞和激活的树突状细胞（p<0.001）。进一步分析表明，TME 显著富集了免疫检查点基因、免疫基因和免疫途径基因（p<0.01）。从基因表达数据中，我们鉴定出 GZMB、HAVCR2、PRF1 和 TBX2 这四个与训练集和验证集预后不良显著相关的 TME 相关基因（p<0.05）。这些基因可能作为监测肿瘤对免疫检查点抑制剂反应的标志物。
结论
本研究表明，TME 特征可作为评估 SCLC 细胞对免疫治疗反应的标志物。
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