Transcriptome network analysis of inflammation and fibrosis in keloids

JOURNAL OF DERMATOLOGICAL SCIENCE(2024)

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摘要
Background: Keloid (KL) is a common benign skin tumor. KL is typically characterized by significant fibrosis and an intensive inflammatory response. Therefore, a comprehensive understanding of the interactions between cellular inflammation and fibrotic cells is essential to elucidate the mechanisms driving the progression of KL and to develop therapeutics. Objective: Investigate the transcriptome landscape of inflammation and fibrosis in keloid scars. Methods: In this paper, we performed transcriptome sequencing and microRNA (miRNA) sequencing on unselected live cells from six human keloid tissues and normal skin tissues to elucidate a comprehensive transcriptome landscape. In addition, we used single-cell RNA sequencing (scRNA-seq) analysis to analyze intercellular communication networks and enrich fibroblast populations in two additional keloid and normal skin samples to study fibroblast diversity. Results: By RNA sequencing and a miRNA-mRNA-PPI network analysis, we identified miR-615-5p and miR122b-3p as possible miRNAs associated with keloids, as they differed most significantly in keloids. Similarly, COL3A1, COL1A2, THBS2, TNC, IGTA, THBS4, TGFB3 as genes with significant differences in keloid may be associated with keloid development. Using single-cell RNA sequencing data from 24,086 cells collected from normal or keloid, we report reconstructed intercellular signaling network analysis and aggregation to modules associated with specific cell subpopulations at the cellular level for keloid alterations. Conclusions: Our multitranscriptomic dataset delineates inflammatory and fibro heterogeneity of human keloids, underlining the importance of intercellular crosstalk between inflammatory cells and fibro cells and revealing potential therapeutic targets. (c) 2023 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.
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关键词
Keloid,Biological information analysis,Single cell sequencing,MicroRNA sequencing,Transcriptome sequencing
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