dentification of novel insulin resistance related ceRNA network: LncRNA, RP11‐773H22.4; miR-3163, miR-1; mRNAs:RET , IGF1-R, m-TOR, GLUT-4, AKT2 in T2DM and its potential editing by CRISPR/Cas9
Bibliographic record
Abstract
Abstract Background: In this study, we aimed to construct Insilco, a competing endogenous RNAs (ceRNAs) network linked to the pathogenesis of insulin resistance followed by its experimental validation in patients’, matched control and cell line samples. And also, to evaluate the efficacy of CRISPR/Cas9 as a potential therapeutic strategy to modulate the expression of this deregulated network. By applying bioinformatic tools, we identified and verified a ceRNA network panel of lncRNA, miRNAs and mRNAs related to insulin resistance and then we validated its expression in 123 patients’ and 106 matched controls and cell line samples using real time PCR. Results: LncRNA-RP11‐773H22.4, together with RET , IGF1-R and m-TOR mRNAs showed significant upregulation in T2DM compared with matched controls while miRNAs: miR-3163, miR-1 and mRNAs: GLUT-4 and AKT2 expression displayed marked downregulation in diabetic samples.Two guide RNAs were designed to target the sequence flanking LncRNA/miRNAs interaction by CRISPER/Cas9 in cell culture. Gene editing tool efficacy was assessed by measurement of the network downstream proteins, GLUT4 and mTOR by immunofluorescence. CRISPR/Cas9 successfully knockout LncRNA-RP11-773H22.4 as evidenced by the reversal of the gene expression of the identified network at RNA and protein levels to the normal expression pattern after gene editing. Conclusion: The presented study provides the significance of this ceRNA based network and its related target genes panel both in the pathogenesis of insulin resistance and as a therapeutic target for gene editing in T2DM.
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 | 0.000 | 0.000 |
| 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".