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Enregistrement W3197841440 · doi:10.1093/neuonc/noab205

Single-cell transcriptome and genome analysis: A much-needed tool for pituitary neuroendocrine tumor studies

2021· letter· en· W3197841440 sur OpenAlexaff
L. Sylvia, Özgür Mete

Notice bibliographique

RevueNeuro-Oncology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiquePituitary Gland Disorders and Treatments
Établissements canadiensUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésTranscriptomeComputational biologyGenomePituitary tumorsBiologyBioinformaticsGeneticsGeneEndocrinologyGene expression

Résumé

récupéré en direct d'OpenAlex

See article by Cui et al. pp 1859–1871 Students who would enter in to the field of endocrine, tread with caution, best be wary of the gland pituitary. Quartered squarely in the head of the pit, it can be said, as a metabolic proctor, it outsmarts the smartest doctor. (The New England Journal of Medicine) The pituitary gland, considered to be the “master” gland because of its importance in regulating hormonal function, is a tiny but powerful structure located at the base of the brain. Tumors of this gland are increasingly recognized.1 Our understanding of the cytogenesis of tumors derived from adenohypophysial cells has grown exponentially. Initially, hormone production was paramount2; the current complex classification is based on cell lineage as defined by expression of transcription factors, patterns of hormone production, and additional measures of cytodifferentiation shown by other features such as keratins and E-cadherin.1 The role of genetic and epigenetic regulators in tumorigenesis has expanded the spectrum of pathogenetic factors beyond the known familial predisposition genes.3 As in other tumors, studies of genomic and proteomic profiling have been attempted to further clarify the characteristics of these increasingly common and complex lesions.4 However, the nature of the pituitary and its tumors is so complex that it remains true indeed that the pituitary outsmarts the brightest and best investigators.5 In the study by Cui et al, “Single-cell transcriptome and genome analyses of pituitary neuroendocrine tumors,” 6 the authors have successfully addressed one of the major limitations of previous studies. In fact, the pituitary is a complex gland composed of at least 6 different hormone-producing cell types in addition to the usual panoply of stromal and vascular cells, including in this case unusual S100-positive sustentacular cells. Tumors in this tiny gland grow by gradually infiltrating around nontumorous tissue, trapping nontumorous elements.7 Thus traditional studies using pieces of tissue, despite claims of being “morphologically characterized as tumor,” are usually contaminated with nontumorous cells that can skew the results of these meticulous analyses,5 leading to incorrect results. In this landmark study, using high-precision single-cell RNA sequencing, Cui et al analyzed 2679 individual cells obtained from 23 surgically resected samples of the major subtypes of PitNETs from 21 patients. They then proceeded to perform single-cell multi-omics sequencing on 238 cells from 5 patients. This study shows the precision required to properly identify the features of tumor cells that may be a homogeneous population but may also be heterogeneous.1 This study has identified that differentially expressed genes of gonadotroph tumors are predominantly downregulated, while those of somatotroph and lactotroph tumors are mainly upregulated and they also identified that plurihormonal tumors show little transcriptomic heterogeneity; this is a fascinating result that correlates with what clinicians have recognized for many years about the distinctions between functioning and nonfunctioning PitNETs. This study also identified novel genes that may be implicated in pituitary tumorigenesis, including AMIGO2, ZFP36, BTG1, and DLG5, potentially opening the door to new avenues for investigation of pathogenesis and therapy for aggressive PitNETs. The approach used by Cui et al is to be commended, as it takes into account the complexity of the structure under investigation before applying expensive and time-consuming technology. It serves as a model for much-needed translational studies of this important gland that will allow progress in a field that has been mired in contradictory and confusing data. The text is the sole product of the authors and no third party had input or gave support to its writing. Conflict of interest statement. None declared.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,016
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0040,004
Science ouverte0,0020,002
Intégrité de la recherche0,0100,021
Charge utile insuffisante (le modèle a refusé de juger)0,0050,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,041
Tête enseignante GPT0,296
Écart entre enseignants0,255 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentnon

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