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单细胞特征可识别与患者预后相关的肿瘤微环境因素。

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.

机构信息

UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA.

UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA.

出版信息

Cell Rep Methods. 2024 Jun 17;4(6):100799. doi: 10.1016/j.crmeth.2024.100799.

Abstract

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

摘要

肿瘤的细胞成分及其微环境在肿瘤进展、患者生存和对癌症治疗的反应中起着关键作用。通过单细胞 RNA 测序 (scRNA-seq) 数据揭示大块肿瘤中的全面细胞图谱至关重要,因为它揭示了通过传统癌症分型方法难以识别的内在肿瘤细胞特征。我们的贡献是 scBeacon,它是一种通过整合和聚类多个 scRNA-seq 数据集来提取用于对大块样本上的无关肿瘤数据集进行去卷积的细胞类型特征的工具。通过在癌症基因组图谱 (TCGA) 队列上使用 scBeacon,我们在特定肿瘤类别中发现了与细胞和分子特征相关的信息,其中许多与患者预后相关。我们开发了一个肿瘤细胞类型图谱,根据细胞类型推断来直观地描绘 TCGA 样本之间的关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b78/11228369/1ea70a447296/fx1.jpg

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