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解析乳腺癌与三阴性乳腺癌之间的miRNA-mRNA相互作用图谱:一种综合生物信息学方法。

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.

作者信息

Balasundaram Ambritha, Mitra Tanisha Saurav, Tayubi Iftikhar Aslam, Zayed Hatem, Doss George Priya C

机构信息

Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.

Department of Computer Science, Faculty of Computing and Information Technology, Rabigh (FCITR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.

出版信息

ACS Omega. 2024 May 29;9(23):24379-24395. doi: 10.1021/acsomega.4c00011. eCollection 2024 Jun 11.

Abstract

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

摘要

乳腺癌(BC)在全球范围内被公认为第二大常见癌症形式。它主要影响女性,并可根据特定癌症受体的过表达分为不同类型。在这种情况下涉及的关键受体是人表皮生长因子受体2(HER2)、雌激素受体(ER)和孕激素受体(PR),以及一个特别复杂的亚类,即三阴性乳腺癌(TNBC)。这种亚分类对于乳腺癌的分层至关重要,并为治疗决策过程提供依据。由于缺乏生长因子受体等治疗靶点,TNBC是最具侵袭性的类型。因此,识别诸如miRNA等可靶向的调节因子可为潜在的治疗干预铺平道路。为了识别包括TNBC在内的BC中常见的差异表达mRNA(DE-mRNA),我们利用了来自基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)的两个数据集。通过蛋白质-蛋白质相互作用(PPI)、分子复合物检测(MCODE)、细胞网络中心分析(CytoNCA)和细胞枢纽分析(CytoHubba)确定了显著的DE-mRNA。在此之后,使用mirDIP预测miRNA。我们利用GSE42568、GSE185645和TCGA并识别出159个常见的DE-mRNA。使用Cytoscape插件,我们确定了BC中10个最显著的DE-mRNA。使用mirDIP,确定了10个DE-mRNA的靶miRNA。我们对TNBC的GEO数据集(GSE45498)进行了深入分析,以证实共享的DE-mRNA和DE-miRNA在TNBC中的重要性。我们确定了四个下调的DE-miRNA,包括hsa-miR-802、hsa-miR-1258、hsa-miR-548a-3p和hsa-miR-2053,它们与TNBC显著相关。我们的研究揭示了在BC和TNBC中显著的miRNA-mRNA相互作用,特别是hsa-miR-802/丝氨酸/苏氨酸激酶MELK、hsa-miR-1258/非着丝粒蛋白G(NCAPG)、miR-548a-3p/细胞周期蛋白A2(CCNA2)和hsa-miR-2053/核仁与纺锤体相关蛋白1(NUSAP1)。观察到的hsa-miR-548a-3p下调与BC患者生存率降低相关,强调了它们作为预后指标的潜在效用。此外,包括细胞周期蛋白B2(CCNB2)、泛素结合酶E2C(UBE2C)、MELK和驱动蛋白家族成员2C(KIF2C)在内的mRNA的差异表达与生存结果降低相关,表明它们作为BC和TNBC中治疗干预潜在靶点的关键作用。这些发现突出了特定的调节机制,这些机制可能对理解和治疗这些癌症类型至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8543/11170726/695617aa6a32/ao4c00011_0001.jpg

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