腹膜假性黏液瘤单细胞和大量RNA测序的分子特征。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ye Jin Ha, Seong-Hwan Park, Seon-Kyu Kim, Ka Hee Tak, Jeong-Hwan Kim, Chan Wook Kim, Yong Sik Yoon, Seon-Young Kim, Jong Lyul Lee
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引用次数: 0

摘要

腹膜假性黏液瘤(PMP)是一种罕见的以腹膜腔粘液性腹水为特征的疾病,通常导致预后不良。然而,这种疾病的组学分析仍未得到充分的探索。在这里,我们介绍了5例PMP病例的单细胞转录组学分析,以确定与PMP发病机制相关的细胞类型特异性基因特征。此外,我们提供了来自两个独立队列的大量RNA-seq数据集:19个新鲜冷冻组织样本(12个pmp)和34个福尔马林固定石蜡包埋(FFPE)样本(25个pmp)。我们还提供了90个样品(45个pmp)的组织微阵列(TMA)分析的蛋白质表达数据。我们的单细胞和大量转录组谱,以及TMA验证,揭示了PMP的细胞多样性,突出了PMP细胞内上皮和间充质特征的共存。这些数据集增强了我们对PMP发病机制的理解,为揭示PMP复杂的分子景观提供了宝贵的资源,并有可能通过进一步的研究提高临床应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Molecular characterization of Pseudomyxoma peritonei with single-cell and bulk RNA sequencing.

Pseudomyxoma peritonei (PMP), a rare condition characterized by mucinous ascites in the peritoneal cavity, often leads to a poor prognosis. However, omics profiling of this disease remains significantly underexplored. Here, we present single-cell transcriptomic profiling of five PMP cases to identify cell type-specific gene features associated with PMP pathogenesis. Additionally, we provide bulk RNA-seq datasets from two independent cohorts: 19 fresh frozen tissue samples (12 PMPs) and 34 formalin-fixed paraffin-embedded (FFPE) samples (25 PMPs). We also offer protein expression data from a tissue microarray (TMA) analysis of 90 samples (45 PMPs). Our single-cell and bulk transcriptomic profiles, along with TMA verifications, reveal the cellular diversity of PMP, highlighting the coexistence of epithelial and mesenchymal characteristics within PMP cells. These datasets enhance our understanding of PMP pathogenesis and provide a valuable resource for uncovering the intricate molecular landscape of PMP, with the potential to improve clinical utility through further research.

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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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