单细胞转录组分析揭示骨肉瘤组织中的副斑表达。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Emel Rothzerg, Wenyu Feng, Dezhi Song, Hengyuan Li, Qingjun Wei, Archa Fox, David Wood, Jiake Xu, Yun Liu
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引用次数: 2

摘要

核副斑是由核富集丰富转录本1 (NEAT1)长链非编码RNA收缩的亚核小体,定位于哺乳动物细胞核的染色质间间隙。副斑在肿瘤进展、转移和化疗耐药中起关键作用。迄今为止,有有限的研究结果表明副斑、NEAT1和异质核核糖核蛋白(hnRNPs)直接或间接地在骨肉瘤的进展中起作用。本文采用单细胞rna测序方法分析了6例骨肉瘤肿瘤组织中NEAT1、副斑蛋白(SFPQ、PSPC1和NONO)和hnRNP成员(HNRNPK、HNRNPM、HNRNPR和HNRNPD)基因的表达。规范化数据强调,除了NEAT1在骨髓细胞1和2亚群中高度表达外,副斑转录物在成骨骨肉瘤细胞中高度丰富。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single-Cell Transcriptome Analysis Reveals Paraspeckles Expression in Osteosarcoma Tissues.

Nuclear paraspeckles are subnuclear bodies contracted by nuclear-enriched abundant transcript 1 (NEAT1) long non-coding RNA, localised in the interchromatin space of mammalian cell nuclei. Paraspeckles have been critically involved in tumour progression, metastasis and chemoresistance. To this date, there are limited findings to suggest that paraspeckles, NEAT1 and heterogeneous nuclear ribonucleoproteins (hnRNPs) directly or indirectly play roles in osteosarcoma progression. Herein, we analysed NEAT1, paraspeckle proteins (SFPQ, PSPC1 and NONO) and hnRNP members (HNRNPK, HNRNPM, HNRNPR and HNRNPD) gene expression in 6 osteosarcoma tumour tissues using the single-cell RNA-sequencing method. The normalised data highlighted that the paraspeckles transcripts were highly abundant in osteoblastic OS cells, except NEAT1, which was highly expressed in myeloid cell 1 and 2 subpopulations.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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