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Nucleic Acids Research·· 4 小时前AI 评分62

XYcellAtlas 发布跨癌种单细胞与空间组学的性别差异及 Y 染色体丢失图谱

XYcellAtlas: a cell-type- and spatial-resolved pan-cancer atlas of sex-biased and Y chromosome loss-associated molecular signatures

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XYcellAtlas 是一个整合并多维可视化人体组织性别差异与 Y 染色体丢失(LOY)特征的数据库,覆盖单细胞转录组、表观组和空间转录组数据。该资源汇总了 6764 例患者样本,涵盖 18 种癌症类型、8 种癌前病变和 21 种组织来源,包含超过 2580 万个单细胞和 100 万个空间转录组 spot。研究利用在正常组织数据集上训练的随机森林分类器,量化各细胞类型的 LOY 频率,并系统收录性别偏向和 LOY 相关的分子改变,包括差异表达基因、染色质峰、核心转录因子及其功能注释,以及恶性细胞和肿瘤微环境中的失调特征。

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来源:Nucleic Acids Research · doi.org

论文摘要

Sex is a critical determinant of cancer susceptibility, clinical prognosis, and therapeutic response. Sex chromosome aberrations, particularly somatic loss of the Y chromosome (LOY) in male malignancies, exert profound impacts on transcriptional regulation, tumor immune microenvironment remodeling, and malignant progression, highlighting an urgent demand for comprehensive integration of single–cell and spatial omics profiles dissecting sex-biased and LOY landscapes across human cancers. Here, we developed XYcellAtlas (http://www.xycellatlas.top/), an integrative database for integration and multidimensional visualization of sex disparities and LOY features across human tissues at single-cell transcriptomic, epigenomic, and spatial-transcriptomic resolutions. This resource aggregates data from 6764 patient samples covering 18 cancer types, eight precancerous lesions, and 21 tissue origins, encompassing over 25.8 million single cells and one million spatial-transcriptomic spots. Leveraging random forest classifiers trained on normal tissue datasets, XYcellAtlas quantifies cell-type-specific LOY frequencies across cell types. Furthermore, it systematically catalogs sex-biased and LOY-associated molecular alterations, including differentially expressed genes, chromatin peaks, core transcription factors, as well as their functional annotations and dysregulatory signatures within malignant cells and the tumor microenvironment. In summary, XYcellAtlas provides a unified, all-in-one analytical platform enabling researchers to query, inspect, quantify, and visualize sex-associated and LOY-derived multi-omics features underlying tumor evolutionary processes.