Post-Transcriptional Modifications to miRNAs Undergo Widespread Alterations, Creating a Unique Lung Adenocarcinoma IsomiRome
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) modulate the expression of oncogenes and tumor suppressor genes, functioning as significant epigenetic regulators in cancer. IsomiRs are miRNA molecules that have undergone small modifications during miRNA processing. These modifications can alter an isomiR's binding stability with mRNA targets, and certain isomiRs have been implicated in the development of specific cancers. Still, the isomiRomes of many tissues, including the lung, have not been characterized; Methods: In this study, we analyzed small RNA sequencing data for three cohorts of lung adenocarcinoma (LUAD) and adult non-malignant lung (ANL) samples. RESULTS: We quantified isomiR expression and found 16 A-to-I edited isomiRs expressed in multiple cohorts, as well as 213 5' isomiRs, 128 3' adenylated isomiRs, and 100 3' uridylated isomiRs. Rates of A-to-I editing at editing hotspots correlated with mRNA expression of the editing enzymes ADAR and ADARB1, which were both observed to be deregulated in LUAD. LUAD samples displayed lower overall rates of A-to-I editing and 3' adenylation than ANL samples. Support vector machines and random forest models were trained on one cohort to distinguish ANL and stage I/II LUAD samples using reads per million (RPM) and frequency data for different types of isomiRs. Models trained on A-to-I editing rates at editing hotspots displayed high accuracy when tested on the other two cohorts and compared favorably to classifiers trained on miRNA expression alone; Conclusions: We have identified isomiRs in the human lung and found that their expression differs between non-malignant and tumor tissues, suggesting they hold potential as cancer biomarkers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".