Medical Informatics and Genetics Center (MedInfoGene), Tabnak St. Velenjak Region, P.O. Box: 1987124500, Tehran, Iran.
Breast Cancer. 2022 Nov;29(6):1050-1066. doi: 10.1007/s12282-022-01385-7. Epub 2022 Jul 24.
Deciphering new molecules related to the breast cancer subtypes is crucial for prognosis and determining a better strategy for targeted therapy. In this study, we aimed to model ceRNAs networks in luminal A and luminal B subtypes of breast cancer and then delve deeper into the role of two candidate lncRNAs in breast tumors.
We constructed two networks as a regulatory model based on our previously identified transcription factors (TFs) and miRNAs with associated lncRNAs. Then, we highlighted the role of some lncRNAs in luminal subtypes of breast cancer using available online databases. Furthermore, we empirically quantified the expression levels of two candidate lncRNAs (DRAIC and TP53TG1) in breast tumors and normal tissues.
Here, we proposed a regulatory model for TFs-miRNAs-lncRNAs in luminal subtypes of breast cancer. We found 18 and 17 differentially expressed lncRNAs in luminal A and luminal B subtypes, respectively. Of these lncRNAs, 16 were associated with breast cancer patients' RFS and/or OS rates. Well-known lncRNAs like HOTAIR and MALAT1 were identified as central factors associated with patients' survival rates in both networks. Based on the results acquired from our comprehensive in-silico data analysis, we carried out clinical experiments on two less-known lncRNAs, DRAIC and TP53TG1, and found a significant association between them with luminal subtypes of breast cancer. Interestingly, we discovered a significant association between DRAIC and TP53TG1 lncRNAs with ER- and PR-positive samples and lymph-node invasion in breast cancer patients.
According to the results, DRAIC and TP53TG1 lncRNAs are overexpressed in breast tumors and may play an oncogenic role with a moderate value of prognosis for luminal subtypes of breast cancer.
解析与乳腺癌亚型相关的新分子对于预后和确定靶向治疗的更好策略至关重要。在这项研究中,我们旨在构建腔 A 和腔 B 亚型乳腺癌的 ceRNA 网络,并深入研究两个候选 lncRNA 在乳腺癌肿瘤中的作用。
我们构建了两个网络作为基于我们先前鉴定的转录因子(TFs)和与 lncRNA 相关的 miRNAs 的调控模型。然后,我们利用可用的在线数据库强调了一些 lncRNA 在乳腺癌腔型中的作用。此外,我们通过经验量化了两个候选 lncRNA(DRAIC 和 TP53TG1)在乳腺癌和正常组织中的表达水平。
在这里,我们提出了一个在乳腺癌腔型中 TFs-miRNAs-lncRNA 的调控模型。我们在腔 A 和腔 B 亚型中分别发现了 18 个和 17 个差异表达的 lncRNA。在这些 lncRNA 中,有 16 个与乳腺癌患者的 RFS 和/或 OS 率相关。像 HOTAIR 和 MALAT1 这样的知名 lncRNA 被鉴定为与两个网络中患者生存率相关的核心因素。基于我们全面的计算数据分析结果,我们对两个不太知名的 lncRNA,DRAIC 和 TP53TG1,进行了临床实验,发现它们与乳腺癌的腔型显著相关。有趣的是,我们发现 DRAIC 和 TP53TG1 lncRNA 与 ER 和 PR 阳性样本和乳腺癌患者的淋巴结侵犯之间存在显著关联。
根据结果,DRAIC 和 TP53TG1 lncRNA 在乳腺癌肿瘤中过度表达,可能发挥致癌作用,对乳腺癌腔型的预后具有中等价值。