Noncoding RNA Profile in Reovirus Treated KRAS-Mutated Colorectal Cancer Patients
Bibliographic record
Abstract
Purpose: To investigate the alterations in the expression of noncoding, micro, and small RNA expression during treatment with oncolytic reovirus in KRAS-mutated colorectal cancer. Methods: Oncolytic reovirus treatment was administered in phase 1 clinical trial (NCT01274624) for 5 days every 28 days, and blood samples were collected before the administration of the reovirus and 48 h, 8 days, and 15 days after its administration on day 1. Data from the blood samples were sorted using Transcriptome Analysis Software (TAC) 4.0, where a two-tailed t-test and a fold change filter were used to ascertain which sample signals had a statistically significant relative fold change of greater than 2 at multiple timepoints before or after oncolytic reovirus administration. Results: The long noncoding RNA’s RP11-332M2.1 (−6.1 x), LINC01506 (−16.18 x), and LINC00534 (−1.94 x) were downregulated at 48 h after reovirus administration [p < 0.05]. ncRNA’s EPB41L4A-AS1 (−6.34 x, 48 h; 11.99 x, day 8), JAK2 (2.2 x, 48 h; −2.23 x, day 8), ANXA4 (20.47 x, day 8; −7.54 x, day 15), and PCDH9 (−2.09, day 8; 1.82 x, day 15) were affected by the reovirus treatment and reflected the progress of the treatment [p < 0.05]. The small RNA SNORA26 (−1.59 x, day 8) was downregulated 48 h after the reovirus administration [p < 0.05]. The microRNA MIR-4461 (6.18 x, day 8; −3.76 x, day 15) was also affected by the reovirus administration [p < 0.05]. Conclusion: The administration of oncolytic reovirus to treat KRAS-mutated colorectal cancer is reflected in a noncoding RNA profile, and expression levels of the ncRNAs in that profile may thus be able to be used as a potential predictive marker for reovirus-treated colorectal cancer.
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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 | 0.000 |
| 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".