Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma.

Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observed that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score.

Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes.

Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways.

背景：肺腺癌是呼吸系统最常见的恶性肿瘤之一，在所有癌症中发病率和死亡率均居首位。本研究旨在建立一个铁死亡相关基因的预后模型，以研究肺腺癌的潜在预后。

方法：我们从癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据库中获得了与肺腺癌临床数据相匹配的基因表达数据。从铁死亡数据库中的三个亚组下载铁死亡相关基因 (FRG)。使用基因表达差异分析、单变量 Cox 回归和 LASSO 回归分析，确定了 7 个具有预后意义的 FRG。利用多元Cox分析结果计算回归系数，建立风险评分公式，将肺腺癌患者分为高危组和低危组。TCGA 结果使用 GEO 数据集进行了验证。然后我们观察d分为低风险组的患者比另一组的总生存期 (OS) 更长。然后我们开发了一个新的列线图，包括年龄、性别、临床分期、TNM 分期和风险评分。

结果：该模型预测的 3 年和 5 年 OS 的曲线下面积 (AUC) 分别为 0.823 和 0.852。校准图和决策曲线分析也证实了该模型的出色预测性能。随后，基因功能富集分析表明，鉴定出的 FRGs 在 DNA 复制、细胞周期调控、细胞粘附、染色体突变、氧化磷酸化、P53 信号通路和蛋白酶体过程中具有重要意义。

结论： 我们的结果证实了 FRGs 在肺腺癌患者中的预后意义，它可能通过多种途径调节肿瘤进展。