构建基于基因组不稳定性的非小细胞肺癌患者预测预后特征。
Construction of a genomic instability-derived predictive prognostic signature for non-small cell lung cancer patients.
机构信息
Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng City, Yancheng, Jiangsu 224006, China.
Department of Respiratory and Critical Care Medicine, Zigong First People's Hospital, Ziliujing District, Zigong, Sichuan 643000, China.
出版信息
Cancer Genet. 2023 Nov;278-279:24-37. doi: 10.1016/j.cancergen.2023.07.008. Epub 2023 Aug 2.
BACKGROUND
Genomic instability (GI) is an effective prognostic marker of cancer. Thus, in this work, we aimed to explore the impact of GI derived signature on prognosis in non-small cell lung cancer (NSCLC) patients using bioinformatics methods.
METHODS
The data of NSCLC patients were collected from The Cancer Genome Atlas. Totally 1794 immune related genes were downloaded from immport database. The optimal prognosis related genes were identified by univariate and LASSO Cox analyses. The risk score model was built to predict the NSCLC patients' prognosis. The immune cell infiltration was analyzed in CIBERSORT.
RESULTS
The 951 differentially expressed genes (DEGs) between the genomic stability (GS) and GI groups were enriched in 862 Gene ontology terms and 32 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the 13 optimal genes, a prognostic risk score mode for NSCLC was established, and the high-risk patients exhibited worse overall survival. Moreover, the nomogram could reliably predict the clinical outcomes. The immune cell infiltration and checkpoints were significantly differential between the two groups (high-risk and low-risk).
CONCLUSION
The GI related 13-gene signature (TMPRSS11E, TNNC2, HLF, FOXM1, PKMYT1, TCN1, RGS20, SYT8, CD1B, LY6K, MFSD4A, KLRG2 APCDD1L) could reliably predict the prognosis of NSCLC patients.
背景
基因组不稳定性（GI）是癌症的一种有效预后标志物。因此，在这项工作中，我们旨在使用生物信息学方法探讨 GI 衍生特征对非小细胞肺癌（NSCLC）患者预后的影响。
方法
从癌症基因组图谱（TCGA）中收集 NSCLC 患者的数据。从 immport 数据库中总共下载了 1794 个免疫相关基因。通过单变量和 LASSO Cox 分析确定与预后相关的最佳基因。建立风险评分模型以预测 NSCLC 患者的预后。通过 CIBERSORT 分析免疫细胞浸润。
结果
GS 组和 GI 组之间的 951 个差异表达基因（DEGs）在 862 个基因本体论术语和 32 个京都基因与基因组百科全书途径中被富集。基于 13 个最佳基因，建立了 NSCLC 的预后风险评分模型，高风险患者的总体生存率更差。此外，该列线图可以可靠地预测临床结局。两组（高风险和低风险）之间的免疫细胞浸润和检查点存在显著差异。
结论
GI 相关的 13 个基因特征（TMPRSS11E、TNNC2、HLF、FOXM1、PKMYT1、TCN1、RGS20、SYT8、CD1B、LY6K、MFSD4A、KLRG2 APCDD1L）可可靠地预测 NSCLC 患者的预后。
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