Department of Thyroid and Breast Surgery, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, P.R. China.
Longhua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, P.R. China.
PeerJ. 2022 Jul 14;10:e13641. doi: 10.7717/peerj.13641. eCollection 2022.
Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.
The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, ) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.
In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.
A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).
最近的研究表明，长非编码 RNA（lncRNA）可能在许多恶性肿瘤中发挥关键的调控作用。本研究旨在探讨新型 lncRNA 生物标志物在乳腺癌的诊断和预后中的应用。
从 RNA-seq 中下载癌症基因组图谱（TCGA）数据库的子集，用于比较乳腺癌组织样本与正常对照组之间的分析。此外，本队列研究还收集了抗凝外周血样本。从血浆中提取并测序细胞外囊泡（EV），然后进行分析以确定 lncRNA 的表达谱，并筛选出与癌症相关的差异表达 lncRNA。使用生物信息学（如加权相关网络分析（WGCNA）、基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集、受试者工作特征（ROC）曲线和生存分析）评估 lncRNA 的表达谱及其相关下游 mRNA，以研究这些 EV lncRNA 及其效应物的诊断和预后价值。
本研究通过转录组学和生物信息学技术，从两个组织数据集和新鲜采集的乳腺癌血浆样本中筛选出 41 个与乳腺癌相关的 lncRNA。通过 WGCNA 分析共鉴定出 19 个基因模块，其中 5 个模块与乳腺癌的临床分期显著相关，包括 28 个 lncRNA 候选物。这些 lncRNA 的 ROC 曲线显示，所有候选物的曲线下面积（AUC）均大于 70%。然而，有 8 个 lncRNA 的 AUC>70%，表明联合使用这些 lncRNA 具有良好的诊断价值。此外，生存分析结果表明，两个表达水平较低的 lncRNA 可能预示着乳腺癌的不良预后。通过组织样本验证，C15orf54、AL157935.1、LINC01117 和 SNHG3 被确定为在乳腺癌病变中具有良好的诊断能力，然而，患者血浆 EV 中没有显著差异。此外，生存分析数据还表明，AL355974.2 可能作为一个独立的预后因素和保护因素。
本研究共发现 5 个 lncRNA 可作为乳腺癌患者的生物标志物，包括 4 个诊断标志物（C15orf54、AL157935.1、LINC01117 和 SNHG3）和 1 个潜在的预后标志物（AL355974.2）。
