Department of Medical Oncology, Hunan Cancer Hospital, Changsha, Hunan 410013, P.R. China.
Department of Medical Oncology, The Central Hospital of Hengyang, Hengyang, Hunan 421001, P.R. China.
Mol Med Rep. 2019 May;19(5):4067-4080. doi: 10.3892/mmr.2019.10061. Epub 2019 Mar 19.
The study aimed to elucidate the mechanisms underlying the occurrence and development of lung adenocarcinoma, and to reveal long non‑coding RNA (lncRNA) prognostic factors to identify patients at high risk of disease recurrence or metastasis. Based on extensive RNA sequencing data and clinical survival prognosis information from patients with lung adenocarcinoma, obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases, a co‑expression network of lncRNAs with different expression levels was built using weighted correlation network analysis and MetaDE.ES. The prognostic lncRNAs were identified using the Cox proportional hazards model and Kaplan‑Meier survival curves to construct a risk scoring system. The reliability of the system was confirmed in validation datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the genes significantly associated with the prognostic lncRNAs using gene set enrichment analysis. A total of 58 and 1,633 differentially expressed lncRNAs and mRNAs were identified, respectively. Considering the module stability, annotation, correlation between modules and clinical factors, and the differential expression levels of lncRNAs, 32 differentially expressed lncRNAs were selected from the brown, red, blue, green and yellow modules for subsequent survival analysis. A signature‑based risk scoring system involving five lncRNAs [DIAPH2 antisense RNA 1, FOXN3 antisense RNA 2, long intergenic non‑protein coding RNA 652, maternally expressed 3 and RHPN1 antisense RNA 1 (head to head)] was developed. The system successfully distinguished between low‑ and high‑risk prognostic samples. System effectiveness was further verified using two independent validation datasets. Further KEGG pathway analysis indicated that the target genes of the five prognostic lncRNAs were associated with a number of cellular processes and signaling pathways, including the cell receptor‑mediated signaling and cell adhesion pathways. A five‑lncRNA signature predicts the prognosis of patients with lung adenocarcinoma. These prognostic lncRNAs may be potential diagnostic markers. The present results may help elucidate the pathogenesis of lung adenocarcinoma.
本研究旨在阐明肺腺癌发生和发展的机制，并揭示长链非编码 RNA（lncRNA）的预后因素，以识别疾病复发或转移风险较高的患者。基于从癌症基因组图谱和基因表达综合数据库中获得的肺腺癌患者的广泛 RNA 测序数据和临床生存预后信息，使用加权相关网络分析和 MetaDE.ES 构建了具有不同表达水平的 lncRNA 的共表达网络。使用 Cox 比例风险模型和 Kaplan-Meier 生存曲线鉴定预后 lncRNA，以构建风险评分系统。该系统在验证数据集中得到了验证。使用基因集富集分析对与预后 lncRNA 显著相关的基因进行京都基因与基因组百科全书（KEGG）通路富集分析。共鉴定出 58 个差异表达的 lncRNA 和 1633 个差异表达的 mRNA。考虑到模块稳定性、注释、模块与临床因素之间的相关性以及 lncRNA 的差异表达水平，从棕色、红色、蓝色、绿色和黄色模块中选择了 32 个差异表达的 lncRNA 进行后续生存分析。基于五个 lncRNA [DIAPH2 反义 RNA 1、FOXN3 反义 RNA 2、长非蛋白编码 RNA 652、母系表达 3 和 RHPN1 反义 RNA 1（头对头）] 的签名风险评分系统。该系统成功地区分了低风险和高风险预后样本。使用两个独立的验证数据集进一步验证了系统的有效性。进一步的 KEGG 通路分析表明，五个预后 lncRNA 的靶基因与许多细胞过程和信号通路相关，包括细胞受体介导的信号和细胞黏附途径。五个 lncRNA 特征预测了肺腺癌患者的预后。这些预后 lncRNA 可能是潜在的诊断标志物。本研究结果可能有助于阐明肺腺癌的发病机制。
