Identification and analysis of differentially expressed lncRNAs and their ceRNA networks in DMD/mdx primary myoblasts
Bibliographic record
Abstract
This study explored the significance of long non-coding RNAs (lncRNAs), particularly their role in maintaining dystrophin protein stability and regulating myocyte proliferation and differentiation. The investigation focused on DMD/mdx mouse skeletal muscle primary myoblasts, aiming to identify lncRNAs potential as biomarkers and therapeutic targets for Duchenne muscular dystrophy (DMD). Utilizing CLC Genomics Workbench software, 554 differentially expressed lncRNAs were identified in DMD/mdx mice compared to wild-type (WT) control. Among them, 373 were upregulated, and 181 were downregulated. The study highlighted specific lncRNAs (e.g., 5930430L01Rik, Gm10143, LncRNA1490, LncRNA580) and their potential regulatory roles in DMD key genes like IGF1, FN1, TNNI1, and MYOD1. By predicting miRNA and their connections with lncRNA and mRNA (ceRNA network) using tools such as miRNet, miRSYSTEM and miRCARTA, the study revealed potential indirect regulation of Dystrophin, IGF1R and UTRN genes by identified lncRNAs (e.g. 2310001H17Rik-203, C130073E24Rik-202, LncRNA2767, 5930430L01Rik and LncRNA580). These findings suggest that the identified lncRNAs may play crucial roles in the development and progression of DMD through their regulatory influence on key gene expression, providing valuable insights for potential therapeutic interventions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".