Liu Linjun, Xu Ke, Zhou Yubai
Department of Biotechnology, College of Life Science & Chemistry, Beijing University of Technology, Chaoyang, Beijing, China.
NHC Key Laboratory of Biosafety, China CDC, National Institute for Viral Disease Control and Prevention, Beijing, China.
PeerJ. 2021 Oct 21;9:e12257. doi: 10.7717/peerj.12257. eCollection 2021.
Emerging evidence implicates the correlation of embryonic germline genes with the tumor progress and patient's outcome. However, the prognostic value of these genes in lung adenocarcinoma (LUAD) has not been fully studied. Here we systematically evaluated this issue, and constructed a novel signature and a nomogram associated with embryonic germline genes for predicting the outcomes of lung adenocarcinoma.
The LUAD cohorts retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used as training set and testing set, respectively. The embryonic germline genes were downloaded from the website https://venn.lodder.dev. Then, the differentially expressed embryonic germline genes (DEGGs) between the tumor and normal samples were identified by limma package. The functional enrichment and pathway analyses were also performed by clusterProfiler package. The prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. Survival and Receiver Operating Characteristic (ROC) analyses were performed to validate the model using training set and four testing GEO datasets. Finally, a prognostic nomogram based on the signature genes was constructed using multivariate regression method.
Among the identified 269 DEGGs, 249 were up-regulated and 20 were down-regulated. GO and KEGG analyses revealed that these DEGGs were mainly enriched in the process of cell proliferation and DNA damage repair. Then, 103 DEGGs with prognostic value were identified by univariate Cox regression and further filtered by LASSO method. The resulting sixteen DEGGs were included in step multivariate Cox regression and an eleven embryonic germline gene related signature (EGRS) was constructed. The model could robustly stratify the LUAD patients into high-risk and low-risk groups in both training and testing sets, and low-risk patients had much better outcomes. The multi-ROC analysis also showed that the EGRS model had the best predictive efficacy compared with other common clinicopathological factors. The EGRS model also showed robust predictive ability in four independent external datasets, and the area under curve (AUC) was 0.726 (GSE30219), 0.764 (GSE50081), 0.657 (GSE37745) and 0.668 (GSE72094). More importantly, the expression level of some genes in EGRS has a significant correlation with the progression of LUAD clinicopathology, suggesting these genes might play an important role in the progression of LUAD. Finally, based on EGRS genes, we built and calibrated a nomogram for conveniently evaluating patients' outcomes.
新出现的证据表明胚胎种系基因与肿瘤进展及患者预后相关。然而，这些基因在肺腺癌（LUAD）中的预后价值尚未得到充分研究。在此，我们系统地评估了这个问题，并构建了一个与胚胎种系基因相关的新型特征和列线图，用于预测肺腺癌的预后。
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中检索到的LUAD队列分别用作训练集和测试集。胚胎种系基因从网站https://venn.lodder.dev下载。然后，使用limma软件包鉴定肿瘤样本和正常样本之间差异表达的胚胎种系基因（DEGGs）。还通过clusterProfiler软件包进行功能富集和通路分析。采用最小绝对收缩和选择算子（LASSO）-Cox回归方法构建预后模型。使用训练集和四个测试GEO数据集进行生存分析和受试者工作特征（ROC）分析以验证模型。最后，使用多元回归方法构建基于特征基因的预后列线图。
在鉴定出的269个DEGGs中，249个上调，20个下调。GO和KEGG分析表明，这些DEGGs主要富集于细胞增殖和DNA损伤修复过程中。然后通过单变量Cox回归鉴定出103个具有预后价值的DEGGs，并进一步通过LASSO方法进行筛选。将得到的16个DEGGs纳入逐步多元Cox回归，构建了一个包含11个胚胎种系基因的相关特征（EGRS）。该模型在训练集和测试集中均能将LUAD患者稳健地分为高风险和低风险组，低风险患者的预后要好得多。多ROC分析还表明，与其他常见临床病理因素相比，EGRS模型具有最佳的预测效能。EGRS模型在四个独立的外部数据集中也显示出稳健的预测能力，曲线下面积（AUC）分别为0.726（GSE30219）、0.764（GSE50081）、0.657（GSE37745）和0.6
