Department of Ulcer and Vascular Surgery, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, P.R. China.
Int J Mol Med. 2019 Oct;44(4):1333-1343. doi: 10.3892/ijmm.2019.4303. Epub 2019 Aug 6.
An ever‑increasing number of long noncoding (lnc)RNAs has been identified in breast cancer. The present study aimed to establish an lncRNA signature for predicting survival in breast cancer. RNA expression profiling was performed using microarray gene expression data from the National Center for Biotechnology Information Gene Expression Omnibus, followed by the identification of breast cancer‑related preserved modules using weighted gene co‑expression network (WGCNA) network analysis. From the lncRNAs identified in these preserved modules, prognostic lncRNAs were selected using univariate Cox regression analysis in combination with the L1‑penalized (LASSO) Cox‑proportional Hazards (Cox‑PH) model. A risk score based on these prognostic lncRNAs was calculated and used for risk stratification. Differentially expressed RNAs (DERs) in breast cancer were identified using MetaDE. Gene Set Enrichment Analysis pathway enrichment analysis was conducted for these prognostic lncRNAs and the DERs related to the lncRNAs in the preserved modules. A total of five preserved modules comprising 73 lncRNAs were mined. An eight‑lncRNA signature (IGHA1, IGHGP, IGKV2‑28, IGLL3P, IGLV3‑10, AZGP1P1, LINC00472 and SLC16A6P1) was identified using the LASSO Cox‑PH model. Risk score based on these eight lncRNAs could classify breast cancer patients into two groups with significantly different survival times. The eight‑lncRNA signature was validated using three independent cohorts. These prognostic lncRNAs were significantly associated with the cell adhesion molecules pathway, JAK‑signal transducer and activator of transcription 5A pathway, and erbb pathway and are potentially involved in regulating angiotensin II receptor type 1, neuropeptide Y receptor Y1, KISS1 receptor, and C‑C motif chemokine ligand 5. The developed eight‑lncRNA signature may have clinical implications for predicting prognosis in breast cancer. Overall, this study provided possible molecular targets for the development of novel therapies against breast cancer.
越来越多的长非编码 (lnc) RNA 在乳腺癌中被鉴定出来。本研究旨在建立 lncRNA 特征以预测乳腺癌的生存。使用来自国家生物技术信息中心基因表达综合数据库 (National Center for Biotechnology Information Gene Expression Omnibus) 的微阵列基因表达数据进行 RNA 表达谱分析，然后使用加权基因共表达网络分析 (WGCNA) 网络分析鉴定与乳腺癌相关的保存模块。从这些保存模块中确定的 lncRNAs 中，使用单变量 Cox 回归分析结合 L1 惩罚 (LASSO) Cox 比例风险 (Cox-PH) 模型选择预后 lncRNAs。根据这些预后 lncRNAs 计算风险评分并用于风险分层。使用 MetaDE 鉴定乳腺癌中的差异表达 RNA (DER)。对这些预后 lncRNAs 和与保存模块中 lncRNAs 相关的 DERs 进行基因集富集分析通路富集分析。挖掘了包含 73 个 lncRNA 的五个保存模块。使用 LASSO Cox-PH 模型鉴定了一个由八个 lncRNA 组成的特征 (IGHA1、IGHGP、IGKV2-28、IGLL3P、IGLV3-10、AZGP1P1、LINC00472 和 SLC16A6P1)。基于这些八个 lncRNA 的风险评分可将乳腺癌患者分为两组，两组的生存时间有显著差异。使用三个独立的队列验证了该八 lncRNA 特征。这些预后 lncRNAs 与细胞粘附分子途径、JAK-信号转导和转录激活因子 5A 途径以及 erb 途径显著相关，并且可能参与调节血管紧张素 II 受体 1、神经肽 Y 受体 Y1、KISS1 受体和 C-C 基序趋化因子配体 5。开发的八 lncRNA 特征可能对预测乳腺癌的预后具有临床意义。总的来说，本研究为开发针对乳腺癌的新型治疗方法提供了可能的分子靶点。
