Zhang Bao, Wang Shenghan, Fu Zhichao, Gao Qiang, Yang Lin, Lei Zhentao, Shi Yuqiang, Le Kai, Xiong Jie, Liu Siyao, Zhang Jiali, Su Junyan, Chen Jing, Liu Mengyuan, Niu Beifang
Department of Urology, Aerospace Center Hospital, Beijing, China.
ChosenMed Technology (Beijing) Co., Ltd., Beijing, China.
Front Genet. 2023 Jan 9;13:1073232. doi: 10.3389/fgene.2022.1073232. eCollection 2022.
Prostate cancer (PCa) is the second most common cancer among men worldwide. Perineural invasion (PNI) was a prominent characteristic of PCa, which was recognized as a key factor in promoting PCa progression. As a complex and heterogeneous disease, its true condition is difficult to explain thoroughly with conventional bulk RNA sequencing. Thus, an improved understanding of PNI-PCa progression at the single-cell level is needed. In this study, we performed scRNAseq on tumor tissues of three PNI-PCa patients. Principal component analysis (PCA) and Uniform manifold approximation and projection (UMAP) were used to reduce dimensionality and visualize the cellular composition of tumor tissues. The differently expressed genes among each cluster were identified by EdgeR. GO enrichment analysis was used to understand the roles of genes within the clusters. Pseudotime cell trajectory was used to reveal the molecular pathways underlying cell fate decisions and identify genes whose expression changed as the cells underwent transition. We applied CellPhoneDB to identify cell-cell interactions among the epithelial and neural cells in PNI-PCa. Analysis of the ∼17,000 single-cell transcriptomes in three PNI prostate cancer tissues, we identified 12 major cell clusters, including neural cells and two epithelial subtypes with different expression profiles. We found that basal/intermediate epithelial cell subtypes highly expressed PCa progression-related genes, including , and . Pseudotime trajectory analysis showed that luminal epithelial cells could be the initiating cells and transition to based/intermediate cells. Gene ontology (GO) enrichment analysis showed that pathways related to cancer progressions, such as lipid catabolic and fatty acid metabolic processes, were significantly enriched in basal/intermediate cells. Our analysis also suggested that basal/intermediate cells communicate closely with neural cells played a potential role in PNI-PCa progression. These results provide our understanding of PNI-PCa cellular heterogeneity and characterize the potential role of basal/intermediate cells in the PNI-PCa progression.
前列腺癌（PCa）是全球男性中第二常见的癌症。神经周围浸润（PNI）是PCa的一个显著特征，被认为是促进PCa进展的关键因素。作为一种复杂的异质性疾病，其真实情况难以用传统的批量RNA测序彻底解释。因此，需要在单细胞水平上更好地理解PNI-PCa的进展。在本研究中，我们对三名PNI-PCa患者的肿瘤组织进行了单细胞RNA测序（scRNAseq）。主成分分析（PCA）和均匀流形近似与投影（UMAP）用于降维和可视化肿瘤组织的细胞组成。通过EdgeR鉴定每个簇中差异表达的基因。基因本体（GO）富集分析用于了解簇内基因的作用。伪时间细胞轨迹用于揭示细胞命运决定背后的分子途径，并识别随着细胞转变其表达发生变化的基因。我们应用CellPhoneDB来识别PNI-PCa中上皮细胞和神经细胞之间的细胞-细胞相互作用。对三个前列腺癌组织中的约17,000个单细胞转录组进行分析，我们鉴定出12个主要细胞簇，包括神经细胞和两种具有不同表达谱的上皮亚型。我们发现基底/中间上皮细胞亚型高度表达与PCa进展相关的基因，包括 、 和 。伪时间轨迹分析表明，管腔上皮细胞可能是起始细胞并转变为基底/中间细胞。基因本体（GO）富集分析表明，与癌症进展相关的途径，如脂质分解代谢和脂肪酸代谢过程，在基底/中间细胞中显著富集。我们的分析还表明，与神经细胞密切沟通的基底/中间细胞在PNI-PCa进展中发挥了潜在作用。这些结果为我们理解PNI-PCa的细胞异质性提供了依据，并表征了基底/中间细胞在PNI-PCa进展中的潜在作用。
