乳腺癌筛查人群血浆细胞外囊泡的小RNA测序数据。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jun-Luan Mo, Xi Li, Lin Lei, Xiong-Shun Liang, Hong-Hao Zhou, Zhao-Qian Liu, Li-Jun Zhang, Ji-Ye Yin, Wen-Xu Hong
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引用次数: 0

摘要

乳腺癌的发病机制尚不清楚。与细胞外囊泡(ev)相关的小rna已被发现参与肿瘤的发展。探讨ev小rna在乳腺癌中的作用具有重要意义。在这项研究中,我们建立了血浆evs相关的小RNA数据集,其中包括120名乳腺癌筛查阳性的女性和60名阴性的女性。这些小RNA包括2656个mirna, 728个pirna和154个tsrna。这些数据为研究人员探索乳腺早期病变的分子诊断生物标志物提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Small RNA sequencing data of plasma extracellular vesicles in a breast cancer screening population.

Small RNA sequencing data of plasma extracellular vesicles in a breast cancer screening population.

Small RNA sequencing data of plasma extracellular vesicles in a breast cancer screening population.

Small RNA sequencing data of plasma extracellular vesicles in a breast cancer screening population.

The pathogenesis of breast cancer is still unclear. Small RNAs associated with extracellular vesicles (EVs) have been found to be involved in tumor development. It is important to explore the role of EVs small RNAs in breast cancer. In this study, we established a plasma EVS-associated small RNA dataset that included 120 women who were positive for breast cancer screening and 60 women who were negative. These small RNA included 2656 miRNAs, 728 piRNAs, and 154 tsRNAs. These data provide a reference for researchers to explore molecular diagnostic biomarkers for early breast lesions.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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