P0021 Long non-coding RNAs as predictors of response to anti-TNF therapy in Ulcerative Colitis patients
Bibliographic record
Abstract
Abstract Background Long non-coding RNAs (lncRNAs) are transcripts with more than 200 nucleotides that modulate molecular mechanisms in several ways, including epigenetic modification, transcription, post-transcription, translation, and post-translation. We have recently demonstrated the involvement of lncRNAs in the pathogenesis of inflammatory bowel disease (IBD) and their role in the accurate diagnosis of IBD patients 1,2. In this study, we aim to determine the potential of lncRNAs in predicting the response of ulcerative colitis (UC) patients to anti-TNF-α therapy. Methods Twenty-two UC patients who were naïve to Adalimumab biosimilar, CinnoRa enrolled in this prospective cohort study. Colonic tissue samples were collected at baseline and week 14 after treatment. Based on our previous reports, a list of important lncRNAs in IBD pathogenesis was prepared and their expression was evaluated by qRT-PCR. Machine learning algorithms were applied to evaluate the predictive potentiality of lncRNAs in differentiating UC non-responders (UCN) from UC Responders (UCR) at baseline. Results Among analyzed lncRNAs, significant dysregulation of H19 and TUG1 was observed in UCNs compared to UCRs before receiving anti-TNF therapy. Interestingly, this difference between the two patient groups was still detected in the 14th week after treatment. Machine learning algorithm results and receiver operating characteristic curve analysis demonstrated that these two lncRNAs could appropriately discriminate UCN patients from UCR with more than 90% accuracy and AUC > 0.8. Conclusion We concluded that expression profiles of lncRNAs are different in pretreatment lesions of UC patients before receiving anti-TNF therapy. Importantly, lncRNAs are involved in the distinctive molecular response of UC patients to anti-TNF monoclonal antibodies. Furthermore, H19 and TUG1 can serve as biomarkers in predicting the response of UC patients to anti-TNF therapy at baseline. References 1. 2. Heydari R, Karimi P, Meyfour A. Long non-coding RNAs as pathophysiological regulators, therapeutic targets and novel extracellular vesicle biomarkers for the diagnosis of inflammatory bowel disease. Biomed Pharmacother. Jul 2024;176:116868. doi:10.1016/j.biopha.2024.116868
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".