背景:三阴性乳腺癌(TNBC)是最具侵袭性的乳腺癌(BC)亚型。由于肿瘤内异质性高和转移,TNBC预后差,指出需要探索不同的分子亚型和基因调控网络。
方法:从基因表达综合(GEO)数据库下载五个原代BC样品的scRNA-seq数据。使用SeuratR包基于过滤和标准化的数据进行聚类,以识别标记基因,随后使用CellMarker数据库对每个子集进行注释。应用AUCellR包以计算每个上皮细胞的标志评分。用FindAllMarkers筛选每个子集的标记基因,并使用注释可视化和集成发现数据库(DAVID)数据库分析其生物学功能。接下来,使用CellChatR包执行cell-cell通信。为了确定关键的调控基因,进行了单细胞调控网络推断和聚类(SCENIC)分析。最后,通过细胞实验验证了关键调控因子的表达和潜在的生物学功能。
结果:共有29,101个细胞被分为9个细胞亚群,即,成纤维细胞,纤维上皮细胞,上皮细胞1,上皮细胞2,上皮细胞3,内皮细胞,T细胞,血浆B细胞和巨噬细胞。特别是,与非TNBC病理型相比,TNBC病理型中上皮细胞的比例更高,转化生长因子-β(TGF-β)活性更高。此外,四个上皮细胞亚群(标记为硬脂酰辅酶A去饱和酶(SCD1),增殖标志物Ki67(MKI67),膜联蛋白A3(ANXA3)和水通道蛋白5(AQP5)被鉴定为对TNBC致病型的影响最大。细胞间相互作用分析显示,ANXA3上皮细胞亚群通过不同的机制抑制T细胞功能。C-fos基因(FOS)和X-box结合蛋白1(XBP1)被认为是参与TNBC进展的关键调控子。值得注意的是,细胞实验表明,沉默XBP1和过表达FOS可以抑制癌细胞的侵袭。
结论:基于scRNA-seq数据鉴定的4个上皮细胞亚群和2个关键调控子可以帮助探索肿瘤内异质性分子机制并开发有效的TNBC治疗方法。
BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). TNBC has a poor prognosis due to high intratumoral heterogeneity and metastasis, pointing to the need to explore distinct molecular subtypes and gene regulatory networks.
METHODS: The scRNA-seq data of five primary BC samples were downloaded from the Gene Expression Omnibus (GEO) database. Clustering was performed based on filtered and normalized data using the Seurat R package to identify marker genes, which were subsequently annotated to each subset using the CellMarker database. AUCell R package was applied to calculate the hallmark score for each epithelial cell. Marker genes of each subset were screened with FindAllMarkers and their biological functions were analyzed using the Database for Annotation Visualization and Integrated Discovery (DAVID) database. Next, cell-cell communication was performed with the CellChat R package. To identify the key regulatory genes, single-cell regulatory network inference and clustering (SCENIC) analysis was conducted. Finally, the expression and potential biological functions of the key regulatory factors were verified through cellular experiments.
RESULTS: A total of 29,101 cells were classified into nine cell subsets, namely, Fibroblasts, Fibroepithelial cells, Epithelial cells 1, Epithelial cells 2, Epithelial cells 3, Endothelial cells, T cells, Plasma B cells and Macrophages. Particularly, the epithelial cells had a higher proportion and higher transforming growth factor-β (TGF-β) activity in the TNBC pathotype as compared to the non-TNBC pathotype. Furthermore, four epithelial cell subsets (marked as Stearoyl-CoA Desaturase (SCD1), marker of proliferation Ki67 (MKI67), Annexin A3 (ANXA3) and aquaporin 5 (AQP5)) were identified as having the greatest impact on the TNBC pathotype. Cell-cell interaction analysis revealed that ANXA3-epithelial cell subset suppressed the T cell function through different mechanisms. C-fos gene (FOS) and X-box binding protein 1 (XBP1) were considered critical regulons involved in TNBC progression. Notably, cellular experiments demonstrated that silencing XBP1 and overexpressing FOS inhibited cancer cell invasion.
CONCLUSIONS: The four epithelial cell subsets and two critical regulons identified based on the scRNA-seq data could help explore the underlying intratumoral heterogeneity molecular mechanism and develop effective therapies for TNBC.