Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Cell Rep Med. 2023 Feb 21;4(2):100934. doi: 10.1016/j.xcrm.2023.100934. Epub 2023 Feb 7.
Pituitary neuroendocrine tumor (PitNET) is one of the most common intracranial tumors with variable recurrence rate. Currently, the recurrence prediction is unsatisfying and can be improved by understanding the cellular origins and differentiation status. Here, to comprehensively reveal the origin of PitNET, we perform comparative analysis of single-cell RNA sequencing data from 3 anterior pituitary glands and 21 PitNETs. We identify distinct genes representing major subtypes of well and poorly differentiated PitNETs in each lineage. To further verify the predictive value of differentiation biomarkers, we include an independent cohort of 800 patients with an average follow-up of 7.2 years. In both PIT1 and TPIT lineages, poorly differentiated groups show significantly higher recurrence rates while well-differentiated groups show higher recurrence rates in SF1 lineage. Our findings reveal the possible origin and differentiation status of PitNET based on which new differentiation classification is proposed and verified to predict tumor recurrence.
垂体神经内分泌肿瘤（PitNET）是最常见的颅内肿瘤之一，其复发率存在差异。目前，对肿瘤复发的预测并不令人满意，通过了解肿瘤的细胞起源和分化状态可以对其进行改善。为了全面揭示 PitNET 的起源，我们对 3 个垂体前叶和 21 个 PitNET 的单细胞 RNA 测序数据进行了比较分析。我们在每个谱系中确定了代表分化良好和分化不良的 PitNET 主要亚型的独特基因。为了进一步验证分化标志物的预测价值，我们纳入了一个包含 800 例患者的独立队列，平均随访 7.2 年。在 PIT1 和 TPIT 谱系中，分化不良组的复发率明显较高，而在 SF1 谱系中，分化良好组的复发率较高。我们的研究结果揭示了 PitNET 的可能起源和分化状态，并基于此提出了新的分化分类方法，并对其预测肿瘤复发的能力进行了验证。
