Bibliographic record
Abstract
Introduction: Lung neuroendocrine neoplasms (NENs) are rare lung cancers whose pathomechanisms are poorly understood. MicroRNAs (miRNAs) are small, non-coding RNAs (19-24 nucleotides in length) that negatively regulate messenger RNA (mRNA) expression. Each miRNA targets a specific set of mRNAs to regulate. To better understand the molecular biology of lung NENs, we investigated miRNA-mediated mRNA regulation in NEN and non-NEN cell lines. Methods: NEN and non-NEN cell line expression profiles were relative frequency normalized before undergoing outlier and batch detection. We identified the top 1% most highly expressed miRNAs in all cell lines, NEN cell lines, and non-NEN cell lines. Next, we identified differentially expressed miRNAs and mRNAs between NEN and non-NEN types. We used the Bio-miRTa target prediction algorithm to identify the putative mRNA targets of key miRNAs. Key miRNAs are miRNAs uniquely expressed in NENs compared to non-NENs types. We subsequently determined the likely biological pathways of the predicted targets using the g:Profiler software. Results: High and differential expression analyses identified miRs-375, -200c, -100, -141, and -7 to be the key miRNAs in lung NEN cell lines compared to non-NEN types. 8014 differentially expressed mRNAs were identified between NEN and non-NEN types. 3916 predicted mRNA targets were identified, which collectively participated in pathways from 21 different categories of cellular function. Discussion: Comparisons between NEN and non-NEN cell lines identified the key miRNAs of lung NENs to be miRs-375, -200c, -100, -141, and -7. The identified biological pathways of the predicted targets highlight the functional differences between NENs and non-NENs. Conclusions: Our high and differential expression analyses results identified the key miRNAs in lung NEN cell lines. The biological pathways of the predicted mRNA targets of key miRNAs were also identified. The results generated in this study help to differentiate NENs from non-NENs, which provides a deeper insight into the pathomechanisms of lung NENs. These results also expand our current knowledge in lung NEN biology, which may facilitate the future development of NEN specific drugs and diagnostic tests.
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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 | 0.001 |
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".