Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China.
School of Information and Management, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China.
Bioengineered. 2021 Dec;12(1):4556-4568. doi: 10.1080/21655979.2021.1954840.
There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.
关于铁代谢基因在预测肺腺癌（LUAD）预后中的作用的研究较少。因此，我们的研究旨在筛选关键基因，并建立一个能预测肺腺癌患者总生存率的预后特征。从癌症基因组图谱（TCGA）下载了 594 名腺癌患者的 RNA-Seq 数据和相应的临床资料。进一步验证了基因表达综合数据库（GEO）GSE42127 中的数据。利用最小绝对收缩和选择算子（LASSO）的 Cox 回归模型构建多基因预后特征。我们构建了一个包含 12 个基因（HAVCR1、SPN、GAPDH、ANGPTL4、PRSS3、KRT8、LDHA、HMMR、SLC2A1、CYP24A1、LOXL2、TIMP1）的预测特征，将患者分为高风险组和低风险组。生存图结果表明，高风险组和低风险组之间的生存预后差异有统计学意义（TCGA：P < 0.001，GEO：P = 0.001）。单因素和多因素 Cox 回归分析证实，风险值是患者 OS 的预测因子（P < 0.001）。时间依赖性 ROC 曲线下面积（AUC）表明，我们的特征在预测 TCGA 队列的 1 年、3 年和 5 年 OS 时具有较高的真阳性率，分别为 0.735、0.711 和 0.601。此外，功能富集分析中突出了免疫相关途径。总之，我们开发并验证了一个包含 12 个基因的预后特征，该特征可能有助于预测肺腺癌的预后，并为肺癌的精准治疗提供多种靶向选择。
