MicroRNA profiling in canine multicentric lymphoma
Bibliographic record
Abstract
Lymphoma is the most common hematopoietic tumour in dogs and is remarkably similar to the human disease. Tumour biomarker discovery is providing new tools for diagnostics and predicting therapeutic response and clinical outcome. MicroRNAs are small non-coding RNAs that participate in post-transcriptional gene regulation and their aberrant expression can impact genes involved in cancer. The aim of this study was to characterize microRNA expression in lymph nodes and plasma from dogs with multicentric B or T cell lymphoma compared to healthy control dogs. We further compared expression between lymph nodes and corresponding plasma samples and assessed changes in expression at relapse compared to time of diagnosis. Lastly, we investigated microRNAs for association with clinical outcome in patients treated with CHOP chemotherapy. A customized PCR array was utilized to profile 38 canine target microRNAs. Quantification was performed using real time RT-qPCR and relative expression was determined by the delta-delta Ct method. In lymph nodes, there were 16 microRNAs with significantly altered expression for B cell lymphoma and 9 for T cell lymphoma. In plasma, there were 15 microRNAs altered for B cell lymphoma and 3 for T cell lymphoma. The majority of microRNAs did not have correlated expression between lymph node and plasma and only 8 microRNAs were significantly different between diagnosis and relapse. For B cell lymphoma, 8 microRNAs had differential expression in the non-remission group compared to dogs that completed CHOP in complete remission. Four of these microRNAs were also altered in patients that died prior to one-year. Kaplan-Meier survival curves for high versus low microRNA expression revealed that 10 microRNAs were correlated with progression-free survival and 3 with overall survival. This study highlights microRNAs of interest for canine multicentric lymphoma. Future goals include development of microRNA panels that may be useful as biomarkers with the intent to provide improved outcome prediction to veterinary cancer patients.
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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 | 0.001 | 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) | 0.000 | 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".