Molecular characterization of Pseudomyxoma peritonei with single-cell and bulk RNA sequencing.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-05 DOI:10.1038/s41597-025-04561-4

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

Abstract

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 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.