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在器官嗜性调节的三阴性乳腺癌转移中使用网络分析鉴定mRNA和非编码RNA枢纽

Identification of mRNA and non-coding RNA hubs using network analysis in organ tropism regulated triple negative breast cancer metastasis.

作者信息

Banerjee Satarupa, Kalyani Yabalooru Surya Radhika, Karunagaran Devarajan

机构信息

Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India; School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.

Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India.

出版信息

Comput Biol Med. 2020 Dec;127:104076. doi: 10.1016/j.compbiomed.2020.104076. Epub 2020 Oct 22.

Abstract

Triple negative breast cancer (TNBC) is aggressive in nature, resistant to conventional therapy and often ends in organ specific metastasis. In this study, publicly available datasets were used to identify miRNA, mRNA and lncRNA hubs. Using validated mRNA-miRNA, mRNA-mRNA and lncRNA-miRNA interaction information obtained from various databases, RNA interaction networks for TNBC and its subtype specific as well as organ tropism regulated metastasis were generated. Further, miRNA-mRNA-lncRNA triad classification was performed using social network analysis from subnetworks and visualized using Cytoscape. Survival analysis of the RNA hubs, oncoprint analysis for mRNAs and pathway analysis of the lncRNAs were also performed. Results indicated that two lncRNAs (NEAT1 and CASC7) and four miRNAs (hsa-miR-106b-5p, hsa-miR-148a-3p, hsa-miR-25-3p and hsa-let-7i-5p) were common between hubs identified in TNBC and TNBC associated metastasis. The exclusive hubs for TNBC associated metastasis were hsa-miR-200b-3p, SP1, HSPA4 and RAB1B. HMGA1 was the top ranked hub in mesenchymal subtype associated lung metastasis, while hsa-miR-27a-3p was identified as the top ranked hub mRNA in luminal androgen receptor subtype associated bone metastasis. When lncRNA associated pathway analysis was performed, Hs Cytoplasmic Ribosomal Protein pathway was found to be the most significant and among the selected hubs, CTNND1, SON and hsa-miR-29c emerged as TNBC survival markers. TP53, FOXA1, MTDH and HDGF were found as the top ranked mRNAs in oncoprint analysis. The pipeline proposed for the first time in this study with validated RNA interaction data integration and graph-based learning for miRNA-mRNA-lncRNA triad classification from RNA hubs may aid experimental cost reduction and its successful execution will allow it to be extended to other diseases too.

摘要

三阴性乳腺癌(TNBC)本质上具有侵袭性,对传统疗法耐药,且常以器官特异性转移告终。在本研究中,使用公开可用的数据集来识别miRNA、mRNA和lncRNA枢纽。利用从各种数据库获得的经过验证的mRNA-miRNA、mRNA-mRNA和lncRNA-miRNA相互作用信息,生成了TNBC及其亚型特异性以及器官嗜性调节转移的RNA相互作用网络。此外,使用来自子网的社会网络分析进行miRNA-mRNA-lncRNA三联体分类,并使用Cytoscape进行可视化。还对RNA枢纽进行了生存分析,对mRNA进行了肿瘤印记分析,并对lncRNA进行了通路分析。结果表明,在TNBC和TNBC相关转移中鉴定出的枢纽之间,有两个lncRNA(NEAT1和CASC7)和四个miRNA(hsa-miR-106b-5p、hsa-miR-148a-3p、hsa-miR-25-3p和hsa-let-7i-5p)是共同的。TNBC相关转移的专属枢纽是hsa-miR-200b-3p、SP1、HSPA4和RAB1B。HMGA1是间充质亚型相关肺转移中排名最高的枢纽,而hsa-miR-27a-3p被确定为腔面雄激素受体亚型相关骨转移中排名最高的枢纽mRNA。当进行lncRNA相关通路分析时,发现Hs细胞质核糖体蛋白通路最为显著,在选定的枢纽中,CTNND1、SON和hsa-miR-29c成为TNBC生存标志物。在肿瘤印记分析中,TP53、FOXA1、MTDH和HDGF被发现是排名最高的mRNA。本研究首次提出的管道,通过整合经过验证的RNA相互作用数据并基于图的学习从RNA枢纽进行miRNA-mRNA-lncRNA三联体分类,可能有助于降低实验成本,其成功实施将使其也能扩展到其他疾病。

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