Differentially expressed alternatively spliced genes in skeletal muscle from cancer patients with cachexia
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Bibliographic record
Abstract
BACKGROUND: Alternative splicing (AS) is a post-transcriptional gene regulatory mechanism that contributes to proteome diversity. Aberrant splicing mechanisms contribute to various cancers and muscle-related conditions such as Duchenne muscular dystrophy. However, dysregulation of AS in cancer cachexia (CC) remains unexplored. Our objectives were (i) to profile alternatively spliced genes (ASGs) on a genome-wide scale and (ii) to identify differentially expressed alternatively spliced genes (DASGs) associated with CC. METHODS: Rectus abdominis muscle biopsies obtained from cancer patients were stratified into cachectic cases (n = 21, classified based on International consensus diagnostic framework for CC) and non-cachectic controls (n = 19, weight stable cancer patients). Human transcriptome array 2.0 was used for profiling ASGs using the total RNA isolated from muscle biopsies. Representative DASG signatures were validated using semi-quantitative RT-PCR. RESULTS: We identified 8960 ASGs, of which 922 DASGs (772 up-regulated and 150 down-regulated) were identified at ≥1.4 fold-change and P < 0.05. Representative DASGs validated by semi-quantitative RT-PCR confirmed the primary findings from the human transcriptome arrays. Identified DASGs were associated with myogenesis, adipogenesis, protein ubiquitination, and inflammation. Up to 10% of the DASGs exhibited cassette exon (exon included or skipped) as a predominant form of AS event. We also observed other forms of AS events such as intron retention, alternate promoters. CONCLUSIONS: Overall, we have, for the first time, conducted global profiling of muscle tissue to identify DASGs associated with CC. The mechanistic roles of the identified DASGs in CC pathophysiology using model systems is warranted, as well as replication of findings in independent cohorts.
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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