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垂体神经内分泌肿瘤的单细胞转录组和基因组分析。

Single-cell transcriptome and genome analyses of pituitary neuroendocrine tumors.

机构信息

Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing, China.

Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China.

出版信息

Neuro Oncol. 2021 Nov 2;23(11):1859-1871. doi: 10.1093/neuonc/noab102.

Abstract

BACKGROUND

Pituitary neuroendocrine tumors (PitNETs) are the second most common intracranial tumor. We lacked a comprehensive understanding of the pathogenesis and heterogeneity of these tumors.

METHODS

We performed high-precision single-cell RNA sequencing for 2679 individual cells obtained from 23 surgically resected samples of the major subtypes of PitNETs from 21 patients. We also performed single-cell multi-omics sequencing for 238 cells from 5 patients.

RESULTS

Unsupervised clustering analysis distinguished all tumor subtypes, which was in accordance with the classification based on immunohistochemistry and provided additional information. We identified 3 normal endocrine cell types: somatotrophs, lactotrophs, and gonadotrophs. Comparisons of tumor and matched normal cells showed that differentially expressed genes of gonadotroph tumors were predominantly downregulated, while those of somatotroph and lactotroph tumors were mainly upregulated. We identified novel tumor-related genes, such as AMIGO2, ZFP36, BTG1, and DLG5. Tumors expressing multiple hormone genes showed little transcriptomic heterogeneity. Furthermore, single-cell multi-omics analysis demonstrated that the tumor had a relatively uniform pattern of genome with slight heterogeneity in copy number variations.

CONCLUSIONS

Our single-cell transcriptome and single-cell multi-omics analyses provide novel insights into the characteristics and heterogeneity of these complex neoplasms for the identification of biomarkers and therapeutic targets.

摘要

背景

垂体神经内分泌肿瘤(PitNETs)是第二常见的颅内肿瘤。我们对这些肿瘤的发病机制和异质性缺乏全面的了解。

方法

我们对 21 名患者的 23 个主要 PitNET 亚型的 2679 个单个细胞进行了高精度单细胞 RNA 测序。我们还对 5 名患者的 238 个细胞进行了单细胞多组学测序。

结果

无监督聚类分析区分了所有肿瘤亚型,这与基于免疫组织化学的分类一致,并提供了额外的信息。我们鉴定了 3 种正常内分泌细胞类型:生长激素细胞、催乳素细胞和促性腺激素细胞。肿瘤和匹配正常细胞的比较表明,促性腺激素肿瘤的差异表达基因主要下调,而生长激素细胞和催乳素细胞的差异表达基因主要上调。我们鉴定了一些新的肿瘤相关基因,如 AMIGO2、ZFP36、BTG1 和 DLG5。表达多种激素基因的肿瘤显示出很少的转录组异质性。此外,单细胞多组学分析表明,肿瘤具有相对均匀的基因组模式,拷贝数变异略有异质性。

结论

我们的单细胞转录组和单细胞多组学分析为这些复杂肿瘤的特征和异质性提供了新的见解,有助于鉴定生物标志物和治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b20d/8563320/abd77b86a673/noab102f0001.jpg

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