基于单细胞RNA测序和机器学习对关键银屑病相关基因的转录组学表征
Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.
摘要(中文译文)
银屑病是一种多方面的皮肤及系统性疾病，由遗传、免疫和环境因素之间复杂的相互作用驱动。遗传易感性发挥关键作用，其中IL-17/IL-23免疫轴被认为是核心致病通路。然而，持续的研究不断揭示其他关键驱动因素、细胞因子、细胞内信号网络及潜在治疗靶点。从基因表达综合数据库（GEO）获取了包含银屑病和健康样本的单细胞RNA测序（scRNA-seq）数据集。使用CIBERSORT（通过估计RNA转录本相对子集进行细胞类型鉴定）估算细胞类型比例，并应用加权基因共表达网络分析（WGCNA）探索细胞类型与基因特征之间的相关性。随后采用机器学习算法鉴定出四个银屑病相关关键基因：DEFB4A、GJB2、SERPINB3和SERPINB13。其表达在批量RNA-seq数据集中得到验证。利用scRNA-seq数据，我们进一步研究了这些基因在皮损中的调控作用及其相关通路改变，并提出了靶向治疗策略。一系列算法鉴定出271个与银屑病皮损及基底细胞显著相关的核心基因。机器学习分析将该基因集精炼为银屑病中的四个关键基因：DEFB4A、GJB2、SERPINB3和SERPINB13。这四个银屑病相关驱动基因在皮损皮肤中上调。我们还筛选了靶向这些基因的小分子化合物，提供了潜在的治疗策略。
Abstract
Psoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors. Genetic predisposition plays a pivotal role, with the IL-17/IL-23 immune axis recognized as a central pathogenic pathway. Ongoing research, however, continues to uncover additional critical drivers, cytokines, intracellular signaling networks, and potential therapeutic targets. Single-cell RNA sequencing (scRNA-seq) datasets comprising both psoriatic and healthy samples were obtained from the Gene Expression Omnibus (GEO). Cell-type proportions were estimated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and weighted gene co-expression network analysis (WGCNA) was applied to explore correlations between cell types and gene signatures. Machine learning algorithms were subsequently employed to identify four psoriasis-associated key genes: DEFB4A, GJB2, SERPINB3, and SERPINB13. Their expression was validated in bulk RNA-seq datasets. Using scRNA-seq data, we further investigated the lesional regulatory roles of these genes and their associated pathway alterations, and we proposed targeted therapeutic strategies. A series of algorithms identified 271 hub genes significantly associated with psoriasis lesions and basal cells. Machine learning analysis refined this set to four key genes in psoriasis: DEFB4A, GJB2, SERPINB3, and SERPINB13. These four psoriasis-associated driver genes were upregulated in lesional skin. We also screened small-molecule compounds targeting these genes, offering potential therapeutic strategies.
如何引用
AMA
Weixiang Wang, Qiang Zhang, Suting Xu. Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.. PloS one. 2026; doi:10.1371/journal.pone.0352663.
APA
Weixiang Wang, Qiang Zhang, Suting Xu (2026). Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.. PloS one. https://doi.org/10.1371/journal.pone.0352663
GB/T 7714
Weixiang Wang, Qiang Zhang, Suting Xu. Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning.[J]. PloS one, 2026 doi:10.1371/journal.pone.0352663.
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