Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis

被引:16
|
作者
Tebani, Abdellah [1 ,2 ]
Jotanovic, Jelena [3 ,4 ]
Hekmati, Neda [3 ]
Sivertsson, Asa [1 ]
Gudjonsson, Olafur [5 ]
Engstrom, Britt Eden [6 ]
Wikstrom, Johan [7 ]
Uhlen, Mathias [1 ]
Casar-Borota, Olivera [3 ,4 ]
Ponten, Fredrik [3 ]
机构
[1] KTH Royal Inst Technol, Sci Life Lab, Dept Prot Sci, Stockholm, Sweden
[2] Normandie Univ, Dept Metab Biochem, UNIROUEN, INSERM,U1245,CHU Rouen, F-76000 Rouen, France
[3] Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden
[4] Uppsala Univ Hosp, Dept Clin Pathol, Uppsala, Sweden
[5] Uppsala Univ, Dept Neurosci, Uppsala, Sweden
[6] Uppsala Univ, Dept Med Sci Endocrinol & Mineral Metab, Uppsala, Sweden
[7] Uppsala Univ, Dept Surg Sci, Neuroradiol, Uppsala, Sweden
关键词
PitNET; Transcriptomics; RNA-seq; Pituitary adenoma; Pathology; Omics; PROTEIN EXPRESSION; TRANSCRIPTION; MUTATIONS; CLASSIFICATION; CANCER; DIAGNOSIS; BIOMARKER; HEALTH; GENES; TPIT;
D O I
10.1186/s40478-021-01284-6
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Pituitary neuroendocrine tumors (PitNETs) are common, generally benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNETs can be classified based on the expression pattern of anterior pituitary hormones and three main transcriptions factors (TF), SF1, PIT1 and TPIT that regulate differentiation of adenohypophysial cells. Here, we have extended this classification based on the global transcriptomics landscape using tumor tissue from a well-defined cohort comprising 51 PitNETs of different clinical and histological types. The molecular profiles were compared with current classification schemes based on immunohistochemistry. Our results identified three main clusters of PitNETs that were aligned with the main pituitary TFs expression patterns. Our analyses enabled further identification of specific genes and expression patterns, including both known and unknown genes, that could distinguish the three different classes of PitNETs. We conclude that the current classification of PitNETs based on the expression of SF1, PIT1 and TPIT reflects three distinct subtypes of PitNETs with different underlying biology and partly independent from the expression of corresponding hormones. The transcriptomic analysis reveals several potentially targetable tumor-driving genes with previously unknown role in pituitary tumorigenesis.
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页数:16
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