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.

中文翻译：

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构建非小细胞肺癌患者基因组不稳定性衍生的预测预后特征

背景

基因组不稳定性（GI）是癌症的有效预后标志物。因此，在这项工作中，我们旨在利用生物信息学方法探讨胃肠道衍生特征对非小细胞肺癌（NSCLC）患者预后的影响。

方法

NSCLC 患者的数据收集自癌症基因组图谱。从导入数据库中下载共1794个免疫相关基因。通过单变量和 LASSO Cox 分析确定了最佳预后相关基因。建立风险评分模型来预测NSCLC患者的预后。在 CIBERSORT 中分析免疫细胞浸润。

结果

基因组稳定性 (GS) 和 GI 组之间的 951 个差异表达基因 (DEG) 富含 862 个基因本体术语和 32 个京都基因和基因组百科全书途径。基于13个最佳基因，建立了NSCLC的预后风险评分模式，高风险患者的总生存率较差。此外，列线图可以可靠地预测临床结果。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着差异。

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）可以可靠地预测NSCLC患者的预后。