Small RNAome profiling from human skeletal muscle: novel miRNAs and their targets associated with cancer cachexia
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRs) are small non-coding RNAs that regulate gene (mRNA) expression. Although the pathological role of miRs have been studied in muscle wasting conditions such as myotonic and muscular dystrophy, their roles in cancer cachexia (CC) are still emerging. OBJECTIVES: The objectives are (i) to profile human skeletal muscle expressed miRs; (ii) to identify differentially expressed (DE) miRs between cachectic and non-cachectic cancer patients; (iii) to identify mRNA targets for the DE miRs to gain mechanistic insights; and (iv) to investigate if miRs show potential prognostic and predictive value. METHODS: Study subjects were classified based on the international consensus diagnostic criteria for CC. Forty-two cancer patients were included, of which 22 were cachectic cases and 20 were non-cachectic cancer controls. Total RNA isolated from muscle biopsies were subjected to next-generation sequencing. RESULTS: A total of 777 miRs were profiled, and 82 miRs with read counts of ≥5 in 80% of samples were retained for analysis. We identified eight DE miRs (up-regulated, fold change of ≥1.4 at P < 0.05). A total of 191 potential mRNA targets were identified for the DE miRs using previously described human skeletal muscle mRNA expression data (n = 90), and a majority of them were also confirmed in an independent mRNA transcriptome dataset. Ingenuity pathway analysis identified pathways related to myogenesis and inflammation. qRT-PCR analysis of representative miRs showed similar direction of effect (P < 0.05), as observed in next-generation sequencing. The identified miRs also showed prognostic and predictive value. CONCLUSIONS: In all, we identified eight novel miRs associated with CC.
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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 it