UNASSIGNED: We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR.
UNASSIGNED: In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue.
UNASSIGNED: In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.
■我们使用单细胞RNA测序(scRNA-seq)和空间转录组(ST)来鉴定细胞类型的差异。然后,我们应用scRNA-seq数据来分解来自癌症基因组图谱(TCGA)队列的批量RNA测序(批量RNA-seq)数据中的细胞类型。我们采用了无偏见的机器学习集成算法来开发基于细胞类型制定者的预后签名。最后,我们使用免疫组织化学和qRT-PCR验证了关键基因LY6D的差异表达。
■在这项研究中,我们确定了一种具有高增殖能力的新细胞类型,Prol,富含细胞周期和有丝分裂基因。我们观察到PDAC中Prol细胞的比例显著增加,和Prol细胞与总生存期(OS)和无进展生存期(PFS)降低相关。此外,Prol细胞类型的标记基因,从scRNA-seq数据中确定,在大量RNA-seq数据中上调并与不良预后相关。我们进一步证实,突变型KRAS和TP53与Prol细胞的丰度增加相关,并且这些细胞与PDAC中的免疫抑制和冷肿瘤微环境相关。ST确定了Prol细胞的空间位置。此外,PDAC中Prol细胞比例较低的患者可能从免疫治疗和吉西他滨治疗中获益更多.此外,我们采用了无偏机器学习整合算法来开发Prol特征,该特征可以精确量化Prol细胞的丰度并准确预测预后.最后,我们证实胰腺癌中LY6D蛋白和mRNA的表达明显高于正常胰腺组织。
■总之,通过整合大量RNA-seq和scRNA-seq,我们发现了一种新的增殖细胞类型,Prol,影响PDAC患者的OS和PFS。