Huang Yuanping, Zhao Yanfei, Guan Yinghui
Department of Respiratory Medicine, First Hospital of Jilin University, Changchun, China.
Department of Pediatrics, First Hospital of Jilin University, Changchun, China.
Transl Cancer Res. 2025 May 30;14(5):2900-2915. doi: 10.21037/tcr-24-1479. Epub 2025 May 27.
BACKGROUND: Lung adenocarcinoma (LUAD) represents the most prevalent histological subtype within lung cancer. Nevertheless, the risk of postoperative metastasis and recurrence remains a substantial concern. We aimed to build the cell cycle-related competing endogenous RNA (ceRNA) networks and potential prognosis prediction models of LUAD, which might provide a valuable reference for studying the prognosis of LUAD. METHODS: The RNA sequencing data of LUAD were procured from The Cancer Genome Atlas (TCGA) database and the differentially expressed RNAs were identified from the Ensembl genome browser 96 database [P<0.05 and |log2 fold change (FC)| >1]. The gene expression profile data were acquired from the Gene Expression Omnibus (GEO) repository. A gene set variation analysis was carried out to determine the differentially expressed genes (DEGs) (P<0.05) and a cell cycle-related ceRNA network of LUAD was constructed based on the DEGs. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to acquire the optimized gene combination, a risk score (RS) prognostic risk prediction model was generated subsequently, and a Kaplan-Meier curve was developed to evaluate the efficacy of the RS model. Moreover, we constructed the 3- and 5-year prognostic models of nomogram using R3.6.1 "rms" package, the C-index was counted for accessing predictive capacity. Receiver operating characteristic (ROC) curves were used to evaluate the multiple prognostic risk prediction model. RESULTS: In total, we identified 240 DEGs and constructed the cell cycle-related ceRNA network of LUAD from datasets GSE50081 and GSE37745. Six optimal genes (, , , , and ) related to prognostic were obtained. The C-index values for 3- and 5-year prognostic nomogram models were 0.7665 and 0.7104, respectively, indicating highly accurate predictive capabilities. The area under the curve (AUC) of the combination of RS and clinical factors prognostic risk prediction model was 0.869 in TCGA and 0.770 in GSE50081 dataset. CONCLUSIONS: This research identified six prognostic biomarkers and built the prognostic prediction models of LUAD, which may enhance the comprehension of disease biology, serve as an effective prognostic tool for LUAD and drive novel therapy development potentially.
背景：肺腺癌（LUAD）是肺癌中最常见的组织学亚型。然而，术后转移和复发的风险仍然是一个重大问题。我们旨在构建LUAD的细胞周期相关竞争性内源性RNA（ceRNA）网络和潜在的预后预测模型，这可能为研究LUAD的预后提供有价值的参考。 方法：从癌症基因组图谱（TCGA）数据库获取LUAD的RNA测序数据，并从Ensembl基因组浏览器96数据库中鉴定差异表达的RNA[P<0.05且|log2倍数变化（FC）|>1]。基因表达谱数据从基因表达综合数据库（GEO）存储库中获取。进行基因集变异分析以确定差异表达基因（DEG）（P<0.05），并基于这些DEG构建LUAD的细胞周期相关ceRNA网络。进行最小绝对收缩和选择算子（LASSO）分析以获得优化的基因组合，随后生成风险评分（RS）预后风险预测模型，并绘制Kaplan-Meier曲线以评估RS模型的有效性。此外，我们使用R3.6.1“rms”包构建了列线图的3年和5年预后模型，计算C指数以评估预测能力。使用受试者工作特征（ROC）曲线评估多个预后风险预测模型。 结果：我们总共鉴定出240个DEG，并从数据集GSE50081和GSE37745构建了LUAD的细胞周期相关ceRNA网络。获得了六个与预后相关的最佳基因（、、、、和）。3年和5年预后列线图模型的C指数值分别为0.7665和0.7104，表明具有高度准确的预测能力。RS与临床因素预后风险预测模型组合在TCGA中的曲线下面积（AUC）为0.869，在GSE50081数据集中为0.770。 结论：本研究鉴定了六个预后生物标志物并构建了LUAD的预后预测模型，这可能会增强对疾病生物学的理解，作为LUAD的有效预后工具，并可能推动新疗法的开发。
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