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乳腺癌中 miRNA 主调控因子的鉴定。

Identification of miRNA Master Regulators in Breast Cancer.

机构信息

Laboratorio de Genómica, Instituto Nacional de Cancerología, Tlalpan, CDMX 14080, Mexico.

Servicio de Anatomía Patológica, Instituto Nacional de Cancerología, Tlalpan, CDMX 14080, Mexico.

出版信息

Cells. 2020 Jul 3;9(7):1610. doi: 10.3390/cells9071610.

Abstract

Breast cancer is the neoplasm with the highest number of deaths in women. Although the molecular mechanisms associated with the development of this tumor have been widely described, metastatic disease has a high mortality rate. In recent years, several studies show that microRNAs or miRNAs regulate complex processes in different biological systems including cancer. In the present work, we describe a group of 61 miRNAs consistently over-expressed in breast cancer (BC) samples that regulate the breast cancer transcriptome. By means of data mining from TCGA, miRNA and mRNA sequencing data corresponding to 1091 BC patients and 110 normal adjacent tissues were downloaded and a miRNA-mRNA network was inferred. Calculations of their oncogenic activity demonstrated that they were involved in the regulation of classical cancer pathways such as cell cycle, PI3K-AKT, DNA repair, and k-Ras signaling. Using univariate and multivariate analysis, we found that five of these miRNAs could be used as biomarkers for the prognosis of overall survival. Furthermore, we confirmed the over-expression of two of them in 56 locally advanced BC samples obtained from the histopathological archive of the National Cancer Institute of Mexico, showing concordance with our previous bioinformatic analysis.

摘要

乳腺癌是女性中死亡率最高的肿瘤。尽管与这种肿瘤发展相关的分子机制已被广泛描述,但转移性疾病的死亡率仍然很高。近年来,有几项研究表明 microRNAs 或 miRNAs 调节包括癌症在内的不同生物系统中的复杂过程。在本工作中,我们描述了一组在乳腺癌 (BC) 样本中一致过表达的 61 个 miRNAs,它们调节乳腺癌转录组。通过从 TCGA 下载 miRNA 和 mRNA 测序数据,对 1091 例 BC 患者和 110 例正常相邻组织的数据进行数据挖掘,并推断出一个 miRNA-mRNA 网络。对它们致癌活性的计算表明,它们参与了细胞周期、PI3K-AKT、DNA 修复和 k-Ras 信号等经典癌症途径的调节。通过单变量和多变量分析,我们发现其中 5 个 miRNAs 可作为总生存率的预后标志物。此外,我们在来自墨西哥国家癌症研究所组织病理学档案的 56 例局部晚期 BC 样本中证实了其中两个的过表达,与我们之前的生物信息学分析一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3de5/7407970/f5d47b8244d1/cells-09-01610-g001.jpg

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