Editorial: RNA-Mediated Epigenetic and Transcriptional Regulation
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".