Editorial: RNA-Mediated Epigenetic and Transcriptional Regulation
Notice bibliographique
Résumé
RNA-Mediated Epigenetic and Transcriptional RegulationRNA plays major regulatory roles in many processes beyond transcription, including protein complex formation, maintenance of genomic stability, and establishment of higher-order cellular structures via phase separation.Accordingly, regulation of RNA abundance, processing, and modification can have a profound impact on cellular and organismal biology.In recent years, significant progress has been made to decipher the complex molecular details and functional interplay between RNA and regulatory processes have begun to emerge.These studies have shed light on how perturbation to these mechanisms can yield aberrant biology, including formation of disease states such as cancers.In this Research Topic "RNA-Mediated Epigenetic and Transcriptional Regulation," several articles highlight the importance of RNA-mediated regulation in different pathological contexts such as cancers and retinal degeneration, and developmental/adaptation processes such as sperm development, resistance to pathogens, and adaptation to cold.Altogether, this Research Topic further highlights the importance of RNA as an effector molecule in biology and underscores its biochemical versatility as a regulatory molecule.N 6 -methyladenosine (m6A) is the most prevalent, conserved and abundant co-transcriptional modification observed in eukaryotic mRNAs.It plays important roles in splicing, translation, RNA stability, and higher-order RNA structures (Jiang et al., 2021) among other biological processes.There are several regulatory complexes which "write," "erase," and "read" this modification on RNA molecules.The delicate regulatory control of m6A has been implicated in numerous physiological and pathological conditions, especially in cancers.In this Research Topic, three research papers and a review paper evaluate, identify, and summarize the m6A landscape of several cancers, including melanoma, acute myeloid leukemia (AML), adenocarcinoma of the esophagogastric junction (AEG) and bladder cancer.Du et al. performed computational analyses on over one thousand melanoma patient samples to determine whether there is a relationship between m6A modification and melanoma immunogenicity.By performing clustering analyses and devising a specialized m6A enrichment scoring system, the authors identified distinct expression profiles of m6A machinery and m6A modification patterns that correlate with known immune phenotypes in melanoma.Li et al. used The Cancer Genome Atlas (TCGA) AML cohort and identified seven long non-coding RNAs (lncRNAs), whose expression not only correlated with at least one m6A regulator, but also could be used as prognostic markers for AML and its immunotherapy response in patients.By using matched tumor and normal tissues Huang et al., performed m6A sequencing to generate an m6A map of AEG.Their work identified several AEG-specific m6A sites on important cancer-related mRNAs, suggesting their importance in this cancer.Finally, Liu reviewed m6A modification and its
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,009 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,014 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».