Department of Biochemistry, Faculty of Pharmacy, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
Department of Medical Biochemistry, Faculty of Medicine, Karadeniz Technical University, 61080, Trabzon, Turkey.
Mol Biol Rep. 2021 Mar;48(3):2463-2471. doi: 10.1007/s11033-021-06281-5. Epub 2021 Mar 28.
Breast cancer is a heterogeneous disease, which is the most common malignancy in women. The incidence and mortality rates of breast cancer indicate that it is the leading cause of cancer-related with deaths. circRNAs operate as part of competing endogenous RNAs (ceRNAs) mechanisms, which play critical roles in the different biological processes of breast cancer such as proliferation, migration, and apoptosis. The goal of the present study is to identify the potential predictive biomarker for breast cancer diagnosis in the circRNA network by in vitro and in silico analyzes. 40 miRNAs were obtained from the miRWalk database and their combinatorial target genes (potential ceRNAs) were identified with ComiR. We stated that the cancer-specific circRNA genes in MCF-7 cells using the cancer-specific circRNA (CSDC) database, and obtained the ones showing potential ceRNA activity in our previous analysis among them. Identified genes with remarkable expression differences between BCa and normal breast tissue were determined by the GEPIA database. Moreover, the Spearman correlation test in the GEPIA database was used for the statistical analysis of the relationship between DCAF7 and SOGA1, SOGA1 and AVL 9, DCAF7 and AVL 9 gene pairs. And also, DCAF7, SOGA1, and AVL9 gene expression levels were detected in MCF-7 and MCF-10A cells by RT-qPCR method. DCAF7, SOGA1, and AVL9 gene were significantly more expressed to BCa tissue and MCF-7 cells than normal breast tissue and MCF-10 A cells. And also, DCAF7 and SOGA1, SOGA1 and AVL9, DCAF7 and AVL9 genes pairs were found to be significantly correlated with BCa. These genes may be considered as potential predictive biomarkers to discriminate BCa patients from healthy persons. Our preliminary results can supply a new perspective for in vitro and vivo studies in the future.
乳腺癌是一种异质性疾病,是女性最常见的恶性肿瘤。乳腺癌的发病率和死亡率表明,它是癌症相关死亡的主要原因。circRNAs 作为竞争性内源性 RNA (ceRNA) 机制的一部分发挥作用,在乳腺癌的增殖、迁移和凋亡等不同生物学过程中发挥关键作用。本研究的目的是通过体外和计算机分析在 circRNA 网络中识别乳腺癌潜在的预测生物标志物。从 miRWalk 数据库中获得了 40 个 miRNA,并使用 ComiR 鉴定了它们的组合靶基因(潜在的 ceRNA)。我们使用癌症特异性 circRNA (CSDC) 数据库在 MCF-7 细胞中指出了癌症特异性 circRNA 基因,并在之前的分析中获得了其中具有潜在 ceRNA 活性的基因。通过 GEPIA 数据库确定了在 BCa 和正常乳腺组织之间表达差异显著的基因。此外,还使用 GEPIA 数据库中的 Spearman 相关性检验对 DCAF7 和 SOGA1、SOGA1 和 AVL9、DCAF7 和 AVL9 基因对之间的关系进行了统计分析。还通过 RT-qPCR 方法检测了 MCF-7 和 MCF-10A 细胞中 DCAF7、SOGA1 和 AVL9 基因的表达水平。DCAF7、SOGA1 和 AVL9 基因在 BCa 组织和 MCF-7 细胞中的表达水平明显高于正常乳腺组织和 MCF-10A 细胞。此外,DCAF7 和 SOGA1、SOGA1 和 AVL9、DCAF7 和 AVL9 基因对与 BCa 显著相关。这些基因可能被认为是区分 BCa 患者和健康人的潜在预测生物标志物。我们的初步结果可以为未来的体外和体内研究提供新的视角。